{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T16:01:31Z","timestamp":1765209691640,"version":"3.46.0"},"reference-count":125,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"INTI International University","award":["INTI-FEQS-01-03-2025"],"award-info":[{"award-number":["INTI-FEQS-01-03-2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively.<\/jats:p>","DOI":"10.3390\/a18120772","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:30:07Z","timestamp":1765207807000},"page":"772","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recent Trends in Machine Learning for Healthcare Big Data Applications: Review of Velocity and Volume Challenges"],"prefix":"10.3390","volume":"18","author":[{"given":"Doaa Yaseen","family":"Khudhur","sequence":"first","affiliation":[{"name":"Department of Informatics, College of Computing & Information Technology, Universiti Tenaga Nasional, Putrajaya Campus, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia"},{"name":"Departments of Artificial Intelligence & Information Technology, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9114-7945","authenticated-orcid":false,"given":"Abdul Samad","family":"Shibghatullah","sequence":"additional","affiliation":[{"name":"Department of Informatics, College of Computing & Information Technology, Universiti Tenaga Nasional, Putrajaya Campus, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia"}]},{"given":"Khalid","family":"Shaker","sequence":"additional","affiliation":[{"name":"Departments of Artificial Intelligence & Information Technology, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8293-6130","authenticated-orcid":false,"given":"Aliza","family":"Abdul Latif","sequence":"additional","affiliation":[{"name":"Department of Informatics, College of Computing & Information Technology, Universiti Tenaga Nasional, Putrajaya Campus, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7509-218X","authenticated-orcid":false,"given":"Zakaria Che","family":"Muda","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Quantity Surveying, INTI International University, Nilai 71800, Negeri Sembilan, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.18231\/j.ijca.2025.032","article-title":"AI innovations in anaesthesia: A systematic review of clinical application","volume":"12","author":"Baalann","year":"2025","journal-title":"Indian J. Clin. Anaesth."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1080\/17517575.2020.1812005","article-title":"Big data analytics in healthcare: A systematic literature review","volume":"14","author":"Khanra","year":"2020","journal-title":"Enterp. Inf. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7776","DOI":"10.1109\/ACCESS.2017.2696365","article-title":"Machine learning with big data: Challenges and approaches","volume":"5","author":"Grolinger","year":"2017","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40537-021-00553-4","article-title":"The use of big data analytics in healthcare","volume":"9","author":"Batko","year":"2022","journal-title":"J. Big Data"},{"key":"ref_5","unstructured":"National Center for Biotechnology Information (NCBI) (2023, January 22). Genebank Statistics, Available online: https:\/\/www.ncbi.nlm.nih.gov\/genbank\/statistics\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s10916-021-01747-2","article-title":"COVID-19 imaging tools: How big data is big?","volume":"45","author":"Santosh","year":"2021","journal-title":"J. Med. Syst."},{"key":"ref_7","first-page":"2349","article-title":"Improving healthcare outcomes through machine learning: Applications and challenges in big data analytics","volume":"11","author":"Fatima","year":"2024","journal-title":"Int. J. Adv. Res. Eng. Technol. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Berros, N., El Mendili, F., Filaly, Y., and El Idrissi, Y.E.B.E. (2023). Enhancing digital health services with big data analytics. Big Data Cogn. Comput., 7.","DOI":"10.3390\/bdcc7020064"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Al-Sai, Z.A., Husin, M.H., Syed-Mohamad, S.M., Abdin, R.M.S., Damer, N., Abualigah, L., and Gandomi, A.H. (2022). Explore big data analytics applications and opportunities: A review. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6040157"},{"key":"ref_10","first-page":"480","article-title":"Big data in healthcare research: A survey study","volume":"62","author":"Miah","year":"2022","journal-title":"J. Comput. Inf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1007\/s41060-024-00603-z","article-title":"Data reduction in big data: A survey of methods, challenges and future directions","volume":"20","author":"Khoei","year":"2025","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s40537-015-0030-3","article-title":"Big data analytics: A survey","volume":"2","author":"Tsai","year":"2015","journal-title":"J. Big Data"},{"key":"ref_13","first-page":"118","article-title":"Out of the box: Big data needs the information profession\u2014The importance of validation","volume":"31","author":"James","year":"2014","journal-title":"Bus. Inf. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1377\/hlthaff.28.5.1418","article-title":"From volume to value: Better ways to pay for health care","volume":"28","author":"Miller","year":"2009","journal-title":"Health Aff."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, C., and Chen, J. (2023). Big data analytics in healthcare. Knowledge Technology and Systems: Toward Establishing Knowledge Systems Science, Springer Nature.","DOI":"10.1007\/978-981-99-1075-5_2"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ohlhorst, F.J. (2012). Big Data Analytics: Turning Big Data into Big Money, John Wiley & Sons.","DOI":"10.1002\/9781119205005"},{"key":"ref_17","first-page":"1","article-title":"3D data management: Controlling data volume, velocity, and variety","volume":"6","author":"Laney","year":"2001","journal-title":"META Group Res. Note"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1177\/14759217211036880","article-title":"Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights","volume":"21","author":"Malekloo","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.jksuci.2017.12.007","article-title":"Implications of big data analytics in developing healthcare frameworks\u2014A review","volume":"31","author":"Palanisamy","year":"2019","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"George, M.M., and Rasmi, P.S. (2022, January 20\u201322). Performance comparison of Apache Hadoop and Apache Spark for COVID-19 data sets. Proceedings of the 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT53264.2022.9716232"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kumari, S., and Muthulakshmi, P. (2022). High-performance computation in big data analytics. International Conference on Intelligent Systems Design and Applications, Springer.","DOI":"10.1007\/978-3-031-27440-4_52"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100924","DOI":"10.1016\/j.imu.2022.100924","article-title":"Using machine learning for healthcare challenges and opportunities","volume":"30","author":"Alanazi","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.23876\/j.krcp.2017.36.1.3","article-title":"Medical big data: Promise and challenges","volume":"36","author":"Lee","year":"2017","journal-title":"Kidney Res. Clin. Pract."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alhenawi, E., Al-Sayyed, R., Hudaib, A., and Mirjalili, S. (2022). Feature selection methods on gene expression microarray data for cancer classification: A systematic review. Comput. Biol. Med., 140.","DOI":"10.1016\/j.compbiomed.2021.105051"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s40537-021-00419-9","article-title":"A survey on data-efficient algorithms in the big data era","volume":"8","author":"Adadi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_27","first-page":"9998819","article-title":"Biomedical image classification in a big data architecture using machine learning algorithms","volume":"2021","author":"Tchapga","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1007\/s00530-020-00736-8","article-title":"Leveraging big data analytics in healthcare enhancement: Trends, challenges, and opportunities","volume":"28","author":"Rehman","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"An, Q., Rahman, S., Zhou, J., and Kang, J.J. (2023). A comprehensive review on machine learning in the healthcare industry: Classification, restrictions, opportunities and challenges. Sensors, 23.","DOI":"10.3390\/s23094178"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103825","DOI":"10.1016\/j.medengphy.2022.103825","article-title":"A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data","volume":"105","author":"Azmi","year":"2022","journal-title":"Med. Eng. Phys."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.heliyon.2024.e26652","article-title":"Machine learning algorithms for FPGA implementation in biomedical engineering applications: A review","volume":"10","author":"Altman","year":"2024","journal-title":"Heliyon"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"115750","DOI":"10.1109\/ACCESS.2024.3443520","article-title":"Managing distributed machine learning lifecycle for healthcare data in the cloud","volume":"12","author":"Zeydan","year":"2024","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"27522","DOI":"10.1109\/ACCESS.2022.3146312","article-title":"A survey of machine learning approaches applied to gene expression analysis for cancer prediction","volume":"10","author":"Khalsan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X.-D. (2020). A Matrix Algebra Approach to Artificial Intelligence, Springer.","DOI":"10.1007\/978-981-15-2770-8"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-981-15-5113-0_18","article-title":"Prediction of cardio-vascular disease through cutting-edge deep learning technologies: An empirical study based on TensorFlow, PyTorch and Keras","volume":"Volume 1165","author":"Gupta","year":"2021","journal-title":"International Conference on Innovative Computing and Communications"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"172101","DOI":"10.1007\/s11432-020-3182-1","article-title":"Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment","volume":"65","author":"Dai","year":"2022","journal-title":"Sci. China Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kimm, H., Paik, I., and Kimm, H. (2021, January 20\u201323). Performance comparison of TPU, GPU, CPU on Google Colaboratory over distributed deep learning. Proceedings of the IEEE 14th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC), Singapore.","DOI":"10.1109\/MCSoC51149.2021.00053"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nikoli\u0107, G.S., Dimitrijevi\u0107, B.R., Nikoli\u0107, T.R., and Stoj\u010dev, M.K. (2022, January 16\u201318). A survey of three types of processing units: CPU, GPU, and TPU. Proceedings of the 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Ohrid, North Macedonia.","DOI":"10.1109\/ICEST55168.2022.9828625"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1109\/TPDS.2016.2626289","article-title":"Parallel deep neural network training for big data on Blue Gene\/Q","volume":"28","author":"Chung","year":"2016","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1049\/cdt2.12016","article-title":"Accelerating deep neural networks implementation: A survey","volume":"15","author":"Dhouibi","year":"2021","journal-title":"IET Comput. Digit. Tech."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Khalilian, M., Boroujeni, F.Z., Mustapha, N., and Sulaiman, M.N. (2009, January 8\u201310). K-means divide and conquer clustering. Proceedings of the International Conference on Computer and Automation Engineering, Bangkok, Thailand.","DOI":"10.1109\/ICCAE.2009.59"},{"key":"ref_42","first-page":"1","article-title":"Big data analytics in healthcare\u2014A systematic literature review and roadmap for practical implementation","volume":"8","author":"Imran","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s13634-016-0355-x","article-title":"A survey of machine learning for big data processing","volume":"2016","author":"Qiu","year":"2016","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/MSP.2014.2345536","article-title":"Stochastic approximation vis-a-vis online learning for big data analytics [lecture notes]","volume":"31","author":"Slavakis","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_45","unstructured":"Ta, V.-D., Liu, C.-M., and Nkabinde, G.W. (2016, January 5\u20137). Big data stream computing in healthcare real-time analytics. Proceedings of the IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108836","DOI":"10.1016\/j.comnet.2022.108836","article-title":"A comparative study on online machine learning techniques for network traffic streams analysis","volume":"207","author":"Shahraki","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Luo, Y., Yin, L., Bai, W., and Mao, K. (2020). An appraisal of incremental learning methods. Entropy, 22.","DOI":"10.3390\/e22111190"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, J., Mao, R., Shao, Z., and Zhu, F. (2020, January 13\u201319). Incremental learning in online scenario. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01394"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100182","DOI":"10.1016\/j.fhj.2024.100182","article-title":"Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions","volume":"11","author":"Senthil","year":"2024","journal-title":"Future Healthc. J."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ganatra, H.A. (2025). Machine learning in pediatric healthcare: Current trends, challenges, and future directions. J. Clin. Med., 14.","DOI":"10.3390\/jcm14030807"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112891","DOI":"10.1109\/ACCESS.2023.3323574","article-title":"Harnessing big data analytics for healthcare: A comprehensive review of frameworks, implications, applications, and impacts","volume":"11","author":"Ahmed","year":"2023","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Domenteanu, A., Cibu, B., and Delcea, C. (2024). Mapping the research landscape of Industry 5.0 from a machine learning and big data analytics perspective: A bibliometric approach. Sustainability, 16.","DOI":"10.3390\/su16072764"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3363554","article-title":"Scalable deep learning on distributed infrastructures: Challenges, techniques, and tools","volume":"53","author":"Mayer","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106368","DOI":"10.1016\/j.infsof.2020.106368","article-title":"Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions","volume":"127","author":"Lwakatare","year":"2020","journal-title":"Inf. Softw. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1093\/bioinformatics\/btz470","article-title":"Scaling tree-based automated machine learning to biomedical big data with a feature set selector","volume":"36","author":"Le","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"100356","DOI":"10.1016\/j.bdr.2022.100356","article-title":"Data stream classification based on extreme learning machine: A review","volume":"30","author":"Zheng","year":"2022","journal-title":"Big Data Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"120673","DOI":"10.1016\/j.eswa.2023.120673","article-title":"HEL-MCNN: Hybrid extreme learning modified convolutional neural network for allocating suitable donors for patients with minimized waiting time","volume":"232","author":"Sangeetha","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lahoura, V., Singh, H., Aggarwal, A., Sharma, B., Mohammed, M.A., Dama\u0161evi\u010dius, R., Kadry, S., and Cengiz, K. (2021). Cloud computing-based framework for breast cancer diagnosis using extreme learning machine. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020241"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Malik, H., Anees, T., Naeem, A., Naqvi, R.A., and Loh, W.-K. (2023). Blockchain-federated and deep-learning-based ensembling of capsule network with incremental extreme learning machines for classification of COVID-19 using CT scans. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020203"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Rajendran, S., Khalaf, O.I., Alotaibi, Y., and Alghamdi, S. (2021). MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-03019-y"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"100136","DOI":"10.1016\/j.fraope.2024.100136","article-title":"Optimization of machine learning models through quantization and data bit reduction in healthcare datasets","volume":"8","author":"Goswami","year":"2024","journal-title":"Frankl. Open"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"104796","DOI":"10.1016\/j.micpro.2023.104796","article-title":"High ECG diagnosis rate using novel machine learning techniques with distributed arithmetic (DA)-based gated recurrent units","volume":"98","author":"Sharada","year":"2023","journal-title":"Microprocess. Microsyst."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Al-Amin, M., and Hossain, J. (2024). Machine learning models for chronic kidney disease diagnosis and prediction. Biomed. Signal Process. Control, 87.","DOI":"10.1016\/j.bspc.2023.105368"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"102609","DOI":"10.1016\/j.dsx.2022.102609","article-title":"Is handling unbalanced datasets for machine learning uplift system performance? A case of diabetic prediction","volume":"16","author":"Narwane","year":"2022","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"9107430","DOI":"10.1155\/2022\/9107430","article-title":"PCA-based incremental extreme learning machine (PCA-IELM) for COVID-19 patient diagnosis using chest X-ray images","volume":"2022","author":"Kumar","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MCAS.2021.3071608","article-title":"FPGA acceleration for big data analytics: Challenges and opportunities","volume":"21","author":"Hoozemans","year":"2021","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1080\/03091902.2020.1769758","article-title":"Big data analytics in medical engineering and healthcare: Methods, advances, and challenges","volume":"44","author":"Wang","year":"2020","journal-title":"J. Med. Eng. Technol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s12859-018-2505-7","article-title":"Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks","volume":"19","author":"Sanaullah","year":"2018","journal-title":"BMC Bioinform."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"100520","DOI":"10.1016\/j.smhl.2024.100520","article-title":"EffSVMNet: An efficient hybrid neural network for improved skin disease classification","volume":"34","author":"Sharma","year":"2024","journal-title":"Smart Health"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"103713","DOI":"10.1016\/j.scs.2022.103713","article-title":"An efficient hardware architecture based on an ensemble of deep learning models for COVID-19 prediction","volume":"80","author":"Sakthivel","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"105492","DOI":"10.1016\/j.mejo.2022.105492","article-title":"Efficient hardware design of a deep U-net model for pixel-level ECG classification in healthcare devices","volume":"126","author":"Cheng","year":"2022","journal-title":"Microelectron. J."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1148\/radiol.2018180547","article-title":"Convolutional neural networks for radiologic images: A radiologist\u2019s guide","volume":"290","author":"Soffer","year":"2019","journal-title":"Radiology"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101857","DOI":"10.1016\/j.media.2020.101857","article-title":"Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes","volume":"67","author":"Draelos","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"104939","DOI":"10.1016\/j.micpro.2023.104939","article-title":"Accelerating deep convolutional neural network on FPGA for ECG signal classification","volume":"103","author":"Aruna","year":"2023","journal-title":"Microprocess. Microsyst."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"154999","DOI":"10.1016\/j.aeue.2023.154999","article-title":"Reconfigurable hardware implementation of K-nearest neighbor algorithm on FPGA","volume":"173","author":"Yacoub","year":"2024","journal-title":"AE\u00dc\u2013Int. J. Electron. Commun."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1007\/s11227-017-2222-4","article-title":"Big data analytics enhanced healthcare systems: A review","volume":"76","author":"Shafqat","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"48","DOI":"10.26599\/BDMA.2018.9020031","article-title":"Big data analytics for healthcare industry: Impact, applications, and tools","volume":"2","author":"Kumar","year":"2018","journal-title":"Big Data Min. Anal."},{"key":"ref_78","first-page":"9898831","article-title":"Predicting chronic kidney disease using hybrid machine learning based on Apache Spark","volume":"2022","author":"Othman","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TCBB.2023.3281638","article-title":"Big data analytics on lung cancer diagnosis framework with deep learning","volume":"21","author":"Guan","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"103285","DOI":"10.1016\/j.advengsoft.2022.103285","article-title":"An assessment of machine learning algorithms for healthcare analysis based on improved MapReduce","volume":"173","author":"Sukanya","year":"2022","journal-title":"Adv. Eng. Softw."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Albattah, W., Khan, R.U., Alsharekh, M.F., and Khasawneh, S.F. (2022). Feature selection techniques for big data analytics. Electronics, 11.","DOI":"10.3390\/electronics11193177"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"28808","DOI":"10.1109\/ACCESS.2019.2955754","article-title":"Medical health big data classification based on KNN classification algorithm","volume":"8","author":"Xing","year":"2019","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"100298","DOI":"10.1016\/j.dajour.2023.100298","article-title":"A breast cancer risk prediction and classification model with ensemble learning and big data fusion","volume":"8","author":"Jaiswal","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Orlu, G.U., Abdullah, R.B., Zaremohzzabieh, Z., Jusoh, Y.Y., Asadi, S., Qasem, Y.A.M., Nor, R.N.H., and Mohd Nasir, W.M.H. (2023). A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era. Sustainability, 15.","DOI":"10.3390\/su152115476"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"4763","DOI":"10.1109\/TPAMI.2024.3357847","article-title":"Advances and challenges in meta-learning: A technical review","volume":"46","author":"Vettoruzzo","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s42979-024-03166-9","article-title":"Meta-learning in healthcare: A survey","volume":"5","author":"Rafiei","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","article-title":"AutoML: A survey of the state-of-the-art","volume":"212","author":"He","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"5097","DOI":"10.1007\/s10115-023-01935-1","article-title":"Eight years of AutoML: Categorization, review, and trends","volume":"65","author":"Barbudo","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1002\/med4.75","article-title":"Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare","volume":"2","author":"Yuan","year":"2024","journal-title":"Med. Adv."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"e6986","DOI":"10.1002\/cpe.6986","article-title":"Hybrid optimization-based learning technique for multi-disease analytics from healthcare big data using optimal pre-processing, clustering, and classifier","volume":"34","author":"Parimanam","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"8214","DOI":"10.1109\/ACCESS.2017.2702561","article-title":"Distributed parallel particle swarm optimization for multi-objective and many-objective large-scale optimization","volume":"5","author":"Cao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"101486","DOI":"10.1016\/j.swevo.2024.101486","article-title":"A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization","volume":"86","author":"Wang","year":"2024","journal-title":"Swarm Evol. Comput."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.jnca.2014.07.032","article-title":"Evolutionary optimization: A big data perspective","volume":"59","author":"Bhattacharya","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_94","first-page":"7152","article-title":"Local feature selection for large-scale data sets with limited labels","volume":"35","author":"Yang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/TAI.2021.3077212","article-title":"Dynamic instance-wise joint feature selection and classification","volume":"2","author":"Liyanage","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"106097","DOI":"10.1016\/j.conengprac.2024.106097","article-title":"Data-driven adaptive and stable feature selection method for large-scale industrial systems","volume":"153","author":"Zhu","year":"2024","journal-title":"Control Eng. Pract."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Sakivama, K., Kato, S., Ishikawa, Y., Hori, A., and Monrroy, A. (2018, January 24\u201327). Deep learning on large-scale multicore clusters. Proceedings of the International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Lyon, France.","DOI":"10.1109\/CAHPC.2018.8645860"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.35940\/ijitee.I8439.0881019","article-title":"Rank-based pseudoinverse computation in extreme learning machine for large datasets","volume":"8","author":"Ragala","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1504\/IJWMC.2017.089327","article-title":"Analysis on fast training speed of extreme learning machine and replacement policy","volume":"13","author":"Zhao","year":"2017","journal-title":"Int. J. Wirel. Mob. Comput."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Yang, B. (2021, January 24\u201326). Application of matrix decomposition in machine learning. Proceedings of the IEEE International Conference on Computer Science, Electronics and Information Engineering and Intelligent Control Technology (CEI), Fuzhou, China.","DOI":"10.1109\/CEI52496.2021.9574465"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Derezi\u0144ski, M., and Mahoney, M.W. (2024, January 25\u201329). Recent and upcoming developments in randomized numerical linear algebra for machine learning. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Barcelona, Spain.","DOI":"10.1145\/3637528.3671461"},{"key":"ref_102","first-page":"2692","article-title":"Optimizing hardware-accelerated general matrix\u2013matrix multiplication for CNNs on FPGAs","volume":"67","author":"Ahmad","year":"2020","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1093\/bioinformatics\/btab017","article-title":"Hardware acceleration of genomics data analysis: Challenges and opportunities","volume":"37","author":"Robinson","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501813","article-title":"Federated learning for healthcare: Systematic review and architecture proposal","volume":"13","author":"Antunes","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_105","first-page":"19","article-title":"Federated learning: Applications, challenges, and future directions","volume":"18","author":"Bharati","year":"2022","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3533708","article-title":"Federated learning for healthcare domain\u2014Pipeline, applications, and challenges","volume":"3","author":"Joshi","year":"2022","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.future.2023.02.021","article-title":"Review on the security of federated learning and its application in healthcare","volume":"144","author":"Li","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Tian, F., Yang, J., Zhao, S., and Sawan, M. (2023). NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1093865"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Gautam, A., and Sharma, S. (2024, January 8\u20139). Artificial narrow intelligence-inspired neuromorphic computing for logic operations in healthcare appliances. Proceedings of the 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India.","DOI":"10.1109\/ICCPCT61902.2024.10672744"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Goyal, S.R. (2025). Neuromorphic system for real-time healthcare applications. Primer to Neuromorphic Computing, Academic Press.","DOI":"10.1016\/B978-0-443-21480-6.00011-0"},{"key":"ref_111","first-page":"82","article-title":"A decision-making model based on spiking neural network (SNN) for remote patient monitoring","volume":"13","author":"Cohen","year":"2023","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Yamazaki, K., Vo-Ho, V.-K., Bulsara, D., and Le, N. (2022). Spiking neural networks and their applications: A review. Brain Sci., 12.","DOI":"10.3390\/brainsci12070863"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Shahid, A., and Mushtaq, M. (2020, January 5\u20137). A survey comparing specialized hardware and evolution in TPUs for neural networks. Proceedings of the IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan.","DOI":"10.1109\/INMIC50486.2020.9318136"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3140659.3080246","article-title":"In-Datacenter Performance Analysis of a Tensor Processing Unit","volume":"45","author":"Jouppi","year":"2017","journal-title":"Proc. Int. Symp. Comput. Archit."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/TBCAS.2020.3036081","article-title":"Hardware implementation of deep network accelerators towards healthcare and biomedical applications","volume":"14","author":"Azghadi","year":"2020","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.3390\/eng5030068","article-title":"A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions","volume":"5","author":"Theodorakopoulos","year":"2024","journal-title":"Eng"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/TPDS.2024.3452478","article-title":"Exploring the design space of distributed parallel sparse matrix\u2013multiple vector multiplication","volume":"35","author":"Huang","year":"2024","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1007\/s00607-020-00846-1","article-title":"HPMaX: Heterogeneous parallel matrix multiplication using CPUs and GPUs","volume":"102","author":"Kang","year":"2020","journal-title":"Computing"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"5933","DOI":"10.1007\/s11227-021-04060-4","article-title":"High-performance medical data processing technology based on distributed parallel machine learning algorithm","volume":"78","author":"Liu","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Sharma, S.K., and Dixit, R.J. (2024). Applications of parallel data processing for biomedical imaging. Applications of Parallel Data Processing for Biomedical Imaging, IGI Global.","DOI":"10.4018\/979-8-3693-2426-4.ch001"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/TBDATA.2020.2977326","article-title":"STARK: Fast and scalable Strassen\u2019s matrix multiplication using Apache Spark","volume":"8","author":"Misra","year":"2020","journal-title":"IEEE Trans. Big Data"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Foldi, T., von Csefalvay, C., and Perez, N.A. (2020). JAMPI: Efficient matrix multiplication in Spark using barrier execution mode. Big Data Cogn. Comput., 4.","DOI":"10.20944\/preprints202007.0450.v1"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Mishra, R. (2023, January 19\u201323). Parallel computing techniques for accelerating machine learning algorithms on big data. Proceedings of the International Conference on Power, Energy, Environment and Intelligent Control (PEEIC), Greater Noida, India.","DOI":"10.1109\/PEEIC59336.2023.10451527"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"107","DOI":"10.3103\/S1060992X22010106","article-title":"Understanding the impact of data parallelism on neural network classification","volume":"31","author":"Jini","year":"2022","journal-title":"Opt. Mem. Neural Netw."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"10359","DOI":"10.1007\/s00521-019-04575-1","article-title":"A new algorithm for normal and large-scale optimization problems: Nomadic people optimizer","volume":"32","author":"Salih","year":"2020","journal-title":"Neural Comput. Appl."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/772\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T15:39:39Z","timestamp":1765208379000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/12\/772"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":125,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["a18120772"],"URL":"https:\/\/doi.org\/10.3390\/a18120772","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,8]]}}}