{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:12:35Z","timestamp":1776413555445,"version":"3.51.2"},"reference-count":61,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T00:00:00Z","timestamp":1652832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In today\u2019s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.<\/jats:p>","DOI":"10.3390\/fi14050153","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T11:59:37Z","timestamp":1652875177000},"page":"153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":103,"title":["Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2592-2824","authenticated-orcid":false,"given":"Roseline Oluwaseun","family":"Ogundokun","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3556-9331","authenticated-orcid":false,"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Communication, \u00d8stfold University College, Halden 1757, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mychal","family":"Douglas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Landmark University, Omu Aran 251103, Nigeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, Kaunas 44404, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1007\/s11095-008-9661-9","article-title":"Cancer is a Preventable Disease that Requires Major Lifestyle Changes","volume":"25","author":"Anand","year":"2008","journal-title":"Pharm. Res."},{"key":"ref_2","unstructured":"Wild, C.P., Stewart, B.W., and Wild, C. (2014). World Cancer Report 2014, World Health Organization."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3322\/caac.21208","article-title":"Cancer statistics, 2014","volume":"64","author":"Siegel","year":"2014","journal-title":"CA Cancer J. Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/S1470-2045(12)70137-7","article-title":"Global burden of cancers at-tributable to infections in 2008: A review and synthetic analysis","volume":"13","author":"Ferlay","year":"2012","journal-title":"Lancet Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.4048\/jbc.2012.15.2.230","article-title":"Development of novel breast cancer re-currence prediction model using support vector machine","volume":"15","author":"Kim","year":"2012","journal-title":"J. Breast Cancer"},{"key":"ref_6","first-page":"3","article-title":"Using three machine learning techniques for predicting breast cancer recurrence","volume":"4","author":"Ahmad","year":"2013","journal-title":"J. Health Med. Inf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"117417","DOI":"10.1016\/j.lfs.2020.117417","article-title":"Cell-free miRNAs as non-invasive biomarkers in breast cancer: Significance in early diagnosis and metastasis prediction","volume":"246","author":"Kashyap","year":"2020","journal-title":"Life Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kadry, S., Damasevicius, R., Taniar, D., Rajinikanth, V., and Lawal, I.A. (2021, January 25\u201327). Extraction of tumour in breast MRI using joint thresholding and segmentation\u2014A study. Proceedings of the 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, Chennai, India. ICBSII 2021.","DOI":"10.1109\/ICBSII51839.2021.9445152"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rajinikanth, V., Kadry, S., Taniar, D., Damasevicius, R., and Rauf, H.T. (2021, January 25\u201327). Breast-cancer detection using thermal images with marine-predators-algorithm selected features. Proceedings of the 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, Chennai, India. ICBSII 2021.","DOI":"10.1109\/ICBSII51839.2021.9445166"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Maqsood, S., Dama\u0161evi\u010dius, R., and Maskeli\u016bnas, R. (2022). TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages. Appl. Sci., 12.","DOI":"10.3390\/app12073273"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Azeez, N.A., Towolawi, T., Van der Vyver, C., Misra, S., Adewumi, A., Dama\u0161evi\u010dius, R., and Ahuja, R. (2019). A fuzzy expert system for diagnosing and analyzing human diseases. Advances in Intelligent Systems and Computing, Springer Nature.","DOI":"10.1007\/978-3-030-16681-6_47"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4581272","DOI":"10.1155\/2018\/4581272","article-title":"ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms","volume":"2018","author":"Lauraitis","year":"2018","journal-title":"J. Health Eng."},{"key":"ref_13","unstructured":"Barracliffe, L., Arandjelovic, O., and Humphris, G. (2017, January 20\u201322). A pilot study of breast cancer patients: Can machine learning predict healthcare professionals\u2019 responses to patient emotions. Proceedings of the International Conference on Bioinformatics and Computational Biology, Honolulu, HI, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106804","DOI":"10.1016\/j.measurement.2019.07.032","article-title":"Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study","volume":"149","author":"Hassan","year":"2020","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2736","DOI":"10.1109\/TII.2018.2808190","article-title":"Context-sensitive access in the industrial internet of things (IIoT) healthcare applications","volume":"14","author":"Alturjman","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/JSAC.2020.3020598","article-title":"An Open IoHT-Based Deep Learning Framework for Online Medical Image Recognition","volume":"39","author":"Dourado","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15652","DOI":"10.1109\/JIOT.2020.3038009","article-title":"Efficient Security and Authentication for Edge-Based Internet of Medical Things","volume":"8","author":"Parah","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"156","DOI":"10.4258\/hir.2016.22.3.156","article-title":"Medical internet of things and big data in healthcare","volume":"22","author":"Dimitrov","year":"2016","journal-title":"Healthc. Inform. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"135632","DOI":"10.1109\/ACCESS.2019.2941575","article-title":"An authentic-based privacy preservation protocol for smart e-healthcare systems in IoT","volume":"7","author":"Deebak","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115749","DOI":"10.1109\/ACCESS.2019.2931637","article-title":"Quantifying uncertainty on the internet of medical things and big-data services using intelligence and deep learning","volume":"7","author":"Zahmatkesh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Huang, C., Zhang, G., Chen, S., and Albuquerque, V. (2022). Healthcare Industry 4.0: A Novel Intelligent Multi-sampling Tensor Network for Detection and Classification of Oral Cancer. IEEE Trans. Ind. Inform., 1."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107077","DOI":"10.1016\/j.measurement.2019.107077","article-title":"Hashed Needham Schroeder industrial IoT-based cost-optimized deep secured data transmission in the cloud","volume":"150","author":"Alzubi","year":"2019","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sharma, A., Kulshrestha, S., and Daniel, S. (2017). Machine learning approaches for breast cancer diagnosis and prognosis. 2017 International Conference on Soft Computing and Its Engineering Applications (icSoftComp), IEEE.","DOI":"10.1109\/ICSOFTCOMP.2017.8280082"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.cmpb.2018.01.011","article-title":"Deep convolutional neural networks for breast cancer screening","volume":"157","author":"Chougrad","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1080\/08839514.2021.2001177","article-title":"Systematic review of computing approaches for breast cancer detection based computer aided diagnosis using mammo-gram images","volume":"35","author":"Zebari","year":"2021","journal-title":"Appl. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Amrane, M., Oukid, S., Gagaoua, I., and Ensari, T. (2018). Breast cancer classification using machine learning. 2018 Electric Electronics, Computer Science, Biomedical Engineerings Meeting (EBBT), IEEE.","DOI":"10.1109\/EBBT.2018.8391453"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1038\/s41586-018-0018-1","article-title":"Metabolic enzyme PFKFB4 activates transcriptional coactivator SRC-3 to drive breast cancer","volume":"556","author":"Dasgupta","year":"2018","journal-title":"Nature"},{"key":"ref_28","first-page":"23","article-title":"Feature selection from a biological database for breast cancer prediction and detection using a machine learning classifier","volume":"57","author":"Gupta","year":"2018","journal-title":"J. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yue, W., Wang, Z., Chen, H., Payne, A., and Liu, X. (2018). Machine learning with applications in breast cancer diagnosis and prognosis. Designs, 2.","DOI":"10.3390\/designs2020013"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"012033","DOI":"10.1088\/1757-899X\/495\/1\/012033","article-title":"Machine learning classification techniques for breast cancer diagnosis","volume":"Volume 495","author":"Omondiagbe","year":"2019","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"212","DOI":"10.11648\/j.acm.20180704.15","article-title":"Performance evaluation of machine learning methods for breast cancer prediction","volume":"7","author":"Li","year":"2018","journal-title":"Appl Comput. Math"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106624","DOI":"10.1016\/j.compeleceng.2020.106624","article-title":"Combination of loss functions for robust breast cancer prediction","volume":"84","author":"Hajiabadi","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_33","first-page":"1106","article-title":"Prediction of breast cancer using supervised machine learning techniques","volume":"8","author":"Shravya","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1177\/1748301818756225","article-title":"Prediction of benign and malignant breast cancer using data mining techniques","volume":"12","author":"Chaurasia","year":"2018","journal-title":"J. Algorithms Comput. Technol."},{"key":"ref_35","first-page":"2249","article-title":"XBPF: An extensible breast cancer prognosis framework for predicting susceptibility, recurrence, and survivability","volume":"8","author":"Aavula","year":"2019","journal-title":"Int. J. Eng. Adv. Technol"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.measurement.2019.03.015","article-title":"Feasible analysis of gene expression\u2013a computational-based classification for breast cancer","volume":"140","author":"Nandagopal","year":"2019","journal-title":"Measurement"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patrec.2018.11.004","article-title":"A new nested ensemble technique for automated diagnosis of breast cancer","volume":"132","author":"Abdar","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, L. (2018). Microwave Sensors for Breast Cancer Detection. Sensors, 18.","DOI":"10.3390\/s18020655"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2050007","DOI":"10.1142\/S1469026820500078","article-title":"A Robust Deep Neural Network Based Breast Cancer Detection and Classification","volume":"19","author":"Mansour","year":"2020","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ragab, M., Albukhari, A., Alyami, J., and Mansour, R.F. (2022). Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images. Biology, 11.","DOI":"10.3390\/biology11030439"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lee, K.Y., and Park, J.B. (2006). Application of particle swarm optimization to economic dispatch problem: Advantages and dis-advantages. 2006 IEEE PES Power Systems Conference and Exposition, IEEE.","DOI":"10.1109\/PSCE.2006.296295"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Abu Khurma, R., Aljarah, I., Sharieh, A., Elaziz, M.A., Dama\u0161evi\u010dius, R., and Krilavi\u010dius, T. (2022). A review of the modification strategies of the nature inspired algorithms for feature selection problem. Mathematics, 10.","DOI":"10.3390\/math10030464"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cooney, C., Korik, A., Folli, R., and Coyle, D. (2020). Evaluation of hyperparameter optimization in machine and deep learning methods for decoding imagined speech eeg. Sensors, 20.","DOI":"10.3390\/s20164629"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"129586","DOI":"10.1109\/ACCESS.2020.3009149","article-title":"Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detection","volume":"8","author":"Mostafa","year":"2020","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Raji, I.D., Bello-Salau, H., Umoh, I.J., Onumanyi, A.J., Adegboye, M.A., and Salawudeen, A.T. (2022). Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models. Appl. Sci., 12.","DOI":"10.3390\/app12031186"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_47","unstructured":"George, G., and Raj, V.C. (2011). Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profile. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Maskeliunas, R., Lauraitis, A., Damasevicius, R., and Misra, S. (2021). Multi-class model MOV-OVR for automatic evaluation of tremor disorders in Huntington\u2019s disease. Communications in Computer and Information Science, Springer International Publishing.","DOI":"10.1007\/978-3-030-69143-1_1"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","article-title":"Support vector machines combined with feature selection for breast cancer diagnosis","volume":"36","author":"Akay","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2015","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5176705","DOI":"10.1155\/2019\/5176705","article-title":"Breast cancer detection in the IoT health environment using modified recursive feature selection","volume":"2019","author":"Memon","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lattanzi, E., Donati, M., and Freschi, V. (2022). Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition. Sensors, 22.","DOI":"10.3390\/s22072637"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neucom.2015.12.061","article-title":"Design of experiments and focused grid search for neural network parameter optimization","volume":"186","author":"Pontes","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_54","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.procs.2021.10.052","article-title":"An enhanced intrusion detec-tion system using particle swarm optimization feature extraction technique","volume":"193","author":"Ogundokun","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ogundokun, R.O., Misra, S., Bajeh, A.O., Okoro, U.O., and Ahuja, R. (2022). An Integrated IDS Using ICA-Based Feature Selection and SVM Classification Method. Illumination of Artificial Intel-Ligence in Cybersecurity and Forensics, Springer.","DOI":"10.1007\/978-3-030-93453-8_11"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Alade, O.M., Sowunmi, O.Y., Misra, S., Maskeli\u016bnas, R., and Dama\u0161evi\u010dius, R. (2018). A neural network based expert system for the diagnosis of diabetes mellitus. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-319-74980-8_2"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s11750-010-0152-x","article-title":"Optimization of SVM parameters for recognition of regulatory DNA sequences","volume":"18","author":"Damasevicius","year":"2010","journal-title":"Top"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, M., Jing, W., Lin, J., Fang, N., Wei, W., Wo\u017aniak, M., and Dama\u0161evi\u010dius, R. (2020). NAS-HRIS: Automatic design and architecture search of neural network for semantic segmentation in remote sensing images. Sensors, 20.","DOI":"10.3390\/s20185292"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"8839524","DOI":"10.1155\/2020\/8839524","article-title":"Text messaging-based medical diagnosis using natural language processing and fuzzy logic","volume":"2020","author":"Omoregbe","year":"2020","journal-title":"J. Health Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3846892","DOI":"10.1155\/2018\/3846892","article-title":"EHealth solutions for the integrated healthcare","volume":"2018","author":"Vanagas","year":"2018","journal-title":"J. Health Eng."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/5\/153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:14:00Z","timestamp":1760138040000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/5\/153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,18]]},"references-count":61,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["fi14050153"],"URL":"https:\/\/doi.org\/10.3390\/fi14050153","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,18]]}}}