{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:25:58Z","timestamp":1783783558917,"version":"3.55.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10586-022-03752-7","type":"journal-article","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T20:02:37Z","timestamp":1665691357000},"page":"3657-3672","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9670-3020","authenticated-orcid":false,"given":"Madhusudan G","family":"Lanjewar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jivan S","family":"Parab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman Yusuf","family":"Shaikh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marlon","family":"Sequeira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"3752_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.compbiomed.2017.12.024","volume":"94","author":"UR Acharya","year":"2018","unstructured":"Acharya, U.R., Koh, J.E.W., Hagiwara, Y., Tan, J.H., Gertych, A., Vijayananthan, A., Yaakup, N.A., Abdullah, B.J.J., Bin Mohd Fabell, M.K., Yeong, C.H.: Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Computers in Biology and Medicine. 94, 11\u201318 (2018). https:\/\/doi.org\/10.1016\/j.compbiomed.2017.12.024","journal-title":"Computers in Biology and Medicine."},{"key":"3752_CR2","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1007\/s00521-017-3130-5","volume":"31","author":"M Shahabi","year":"2019","unstructured":"Shahabi, M., Hassanpour, H., Mashayekhi, H.: Rule extraction for fatty liver detection using neural networks. Neural Comput. & Applic. 31, 979\u2013989 (2019). https:\/\/doi.org\/10.1007\/s00521-017-3130-5","journal-title":"Neural Comput. & Applic"},{"key":"3752_CR3","doi-asserted-by":"publisher","first-page":"2783","DOI":"10.1007\/s00521-020-05157-2","volume":"33","author":"L Ali","year":"2021","unstructured":"Ali, L., Wajahat, I., Amiri Golilarz, N., Keshtkar, F., Bukhari, S.A.C.: LDA\u2013GA\u2013SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine. Neural Comput. & Applic. 33, 2783\u20132792 (2021). https:\/\/doi.org\/10.1007\/s00521-020-05157-2","journal-title":"Neural Comput. & Applic"},{"key":"3752_CR4","doi-asserted-by":"publisher","first-page":"e1008244","DOI":"10.1371\/journal.pcbi.1008244","volume":"16","author":"D Grissa","year":"2020","unstructured":"Grissa, D., Nytoft Rasmussen, D., Krag, A., Brunak, S., Juhl Jensen, L.: Alcoholic liver disease: A registry view on comorbidities and disease prediction. PLoS Comput. Biol. 16, e1008244 (2020). https:\/\/doi.org\/10.1371\/journal.pcbi.1008244","journal-title":"PLoS Comput. Biol."},{"key":"3752_CR5","doi-asserted-by":"publisher","first-page":"105551","DOI":"10.1016\/j.cmpb.2020.105551","volume":"196","author":"S Hashem","year":"2020","unstructured":"Hashem, S., ElHefnawi, M., Habashy, S., El-Adawy, M., Esmat, G., Elakel, W., Abdelazziz, A.O., Nabeel, M.M., Abdelmaksoud, A.H., Elbaz, T.M., Shousha, H.I.: Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease. Comput. Methods Programs Biomed. 196, 105551 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105551","journal-title":"Comput. Methods Programs Biomed."},{"key":"3752_CR6","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1038\/s41467-019-14050-z","volume":"11","author":"B Losic","year":"2020","unstructured":"Losic, B., Craig, A.J., Villacorta-Martin, C., Martins-Filho, S.N., Akers, N., Chen, X., Ahsen, M.E., von Felden, J., Labgaa, I., D\u02b9Avola, D., Allette, K., Lira, S.A., Furtado, G.C., Garcia-Lezana, T., Restrepo, P., Stueck, A., Ward, S.C., Fiel, M.I., Hiotis, S.P., Gunasekaran, G., Sia, D., Schadt, E.E., Sebra, R., Schwartz, M., Llovet, J.M., Thung, S., Stolovitzky, G., Villanueva, A.: Intratumoral heterogeneity and clonal evolution in liver cancer. Nat. Commun. 11, 291 (2020). https:\/\/doi.org\/10.1038\/s41467-019-14050-z","journal-title":"Nat. Commun."},{"key":"3752_CR7","doi-asserted-by":"publisher","unstructured":"Naseem, R., Khan, B., Shah, M.A., Wakil, K., Khan, A., Alosaimi, W., Uddin, M.I., Alouffi, B.: Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome. Journal of Healthcare Engineering. 1\u201313 (2020). (2020). https:\/\/doi.org\/10.1155\/2020\/6680002","DOI":"10.1155\/2020\/6680002"},{"key":"3752_CR8","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.compbiomed.2016.02.013","volume":"71","author":"E Goceri","year":"2016","unstructured":"Goceri, E., Shah, Z.K., Layman, R., Jiang, X., Gurcan, M.N.: Quantification of liver fat: A comprehensive review. Computers in Biology and Medicine. 71, 174\u2013189 (2016). https:\/\/doi.org\/10.1016\/j.compbiomed.2016.02.013","journal-title":"Computers in Biology and Medicine."},{"key":"3752_CR9","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1007\/s40846-017-0360-z","volume":"38","author":"M Abdar","year":"2018","unstructured":"Abdar, M., Yen, N.Y., Hung, J.C.-S.: Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees. J. Med. Biol. Eng. 38, 953\u2013965 (2018). https:\/\/doi.org\/10.1007\/s40846-017-0360-z","journal-title":"J. Med. Biol. Eng."},{"key":"3752_CR10","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1038\/s41598-018-20166-x","volume":"8","author":"S Perveen","year":"2018","unstructured":"Perveen, S., Shahbaz, M., Keshavjee, K., Guergachi, A.: A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression. Sci. Rep. 8, 2112 (2018). https:\/\/doi.org\/10.1038\/s41598-018-20166-x","journal-title":"Sci. Rep."},{"key":"3752_CR11","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1002\/hep.30858","volume":"71","author":"JD Yang","year":"2020","unstructured":"Yang, J.D., Ahmed, F., Mara, K.C., Addissie, B.D., Allen, A.M., Gores, G.J., Roberts, L.R.: Diabetes Is Associated With Increased Risk of Hepatocellular Carcinoma in Patients With Cirrhosis From Nonalcoholic Fatty Liver Disease. Hepatology. 71, 907\u2013916 (2020). https:\/\/doi.org\/10.1002\/hep.30858","journal-title":"Hepatology."},{"key":"3752_CR12","first-page":"311","volume":"29","author":"B Muruganantham","year":"2020","unstructured":"Muruganantham, B.: Liver Disease Prediction Using Classification Algorithms. Int. J. Adv. Sci. Technol. 29, 311\u2013319 (2020)","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"3752_CR13","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1016\/j.jestch.2020.01.005","volume":"23","author":"A Kececi","year":"2020","unstructured":"Kececi, A., Yildirak, A., Ozyazici, K., Ayluctarhan, G., Agbulut, O., Zincir, I.: Implementation of machine learning algorithms for gait recognition. Eng. Sci. Technol. Int. J. 23, 931\u2013937 (2020). https:\/\/doi.org\/10.1016\/j.jestch.2020.01.005","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"3752_CR14","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1007\/s00521-019-04041-y","volume":"32","author":"P Govindarajan","year":"2020","unstructured":"Govindarajan, P., Soundarapandian, R.K., Gandomi, A.H., Patan, R., Jayaraman, P., Manikandan, R.: Classification of stroke disease using machine learning algorithms. Neural Comput. & Applic. 32, 817\u2013828 (2020). https:\/\/doi.org\/10.1007\/s00521-019-04041-y","journal-title":"Neural Comput. & Applic"},{"key":"3752_CR15","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s12553-019-00299-3","volume":"9","author":"D Ramesh","year":"2019","unstructured":"Ramesh, D., Katheria, Y.S.: Ensemble method based predictive model for analyzing disease datasets: a predictive analysis approach. Health Technol. 9, 533\u2013545 (2019). https:\/\/doi.org\/10.1007\/s12553-019-00299-3","journal-title":"Health Technol."},{"key":"3752_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i10\/87212","volume":"9","author":"S Godara","year":"2016","unstructured":"Godara, S.: Evaluation of Predictive Machine Learning Techniques as Expert Systems in Medical Diagnosis. IJST. 9, 1\u201314 (2016). https:\/\/doi.org\/10.17485\/ijst\/2016\/v9i10\/87212","journal-title":"IJST."},{"key":"3752_CR17","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1016\/j.jestch.2019.11.002","volume":"23","author":"MS Sanaj","year":"2020","unstructured":"Sanaj, M.S., Joe Prathap, P.M.: Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere. Eng. Sci. Technol. Int. J. 23, 891\u2013902 (2020). https:\/\/doi.org\/10.1016\/j.jestch.2019.11.002","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"3752_CR18","doi-asserted-by":"publisher","first-page":"012029","DOI":"10.1088\/1757-899X\/1022\/1\/012029","volume":"1022","author":"N Tanwar","year":"2021","unstructured":"Tanwar, N., Rahman, K.F.: Machine Learning in liver disease diagnosis: Current progress and future opportunities. IOP Conf. Ser. : Mater. Sci. Eng. 1022, 012029 (2021). https:\/\/doi.org\/10.1088\/1757-899X\/1022\/1\/012029","journal-title":"IOP Conf. Ser. : Mater. Sci. Eng."},{"key":"3752_CR19","doi-asserted-by":"publisher","first-page":"8073","DOI":"10.3390\/ijms22158073","volume":"22","author":"K Jaganathan","year":"2021","unstructured":"Jaganathan, K., Tayara, H., Chong, K.T.: Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. IJMS. 22, 8073 (2021). https:\/\/doi.org\/10.3390\/ijms22158073","journal-title":"IJMS."},{"key":"3752_CR20","doi-asserted-by":"crossref","unstructured":"Thirunavukkarasu, Singh, A.S., Irfan, M., Chowdhury, A.: Prediction of Liver Disease using Classification Algorithms. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA). pp.\u00a01\u20133 (2018)","DOI":"10.1109\/CCAA.2018.8777655"},{"key":"3752_CR21","doi-asserted-by":"publisher","first-page":"032002","DOI":"10.1088\/1742-6596\/1529\/3\/032002","volume":"1529","author":"N Razali","year":"2020","unstructured":"Razali, N., Mustapha, A., Wahab, M.H.A., Mostafa, S.A., Rostam, S.K.: A Data Mining Approach to Prediction of Liver Diseases. J. Phys. : Conf. Ser. 1529, 032002 (2020). https:\/\/doi.org\/10.1088\/1742-6596\/1529\/3\/032002","journal-title":"J. Phys. : Conf. Ser."},{"key":"3752_CR22","doi-asserted-by":"crossref","unstructured":"Ayeldeen, H., Shaker, O., Ayeldeen, G., Anwar, K.M.: Prediction of liver fibrosis stages by machine learning model: A decision tree approach. In: 2015 Third World Conference on Complex Systems (WCCS). pp.\u00a01\u20136 (2015)","DOI":"10.1109\/ICoCS.2015.7483212"},{"key":"3752_CR23","first-page":"8","volume":"8","author":"DH Belavigi","year":"2019","unstructured":"Belavigi, D.H., Veena, G.S., Harekal, D.: Prediction of Liver Disease using Rprop, SAG and CNN. Int. J. Innovative Technol. Exploring Eng. (IJITEE). 8, 8 (2019)","journal-title":"Int. J. Innovative Technol. Exploring Eng. (IJITEE)"},{"key":"3752_CR24","doi-asserted-by":"crossref","unstructured":"Kumar, S., Katyal, S.: Effective Analysis and Diagnosis of Liver Disorder by Data Mining. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). pp.\u00a01047\u20131051 (2018)","DOI":"10.1109\/ICIRCA.2018.8596817"},{"key":"3752_CR25","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1109\/TCBB.2017.2690848","volume":"15","author":"S Hashem","year":"2018","unstructured":"Hashem, S., Esmat, G., Elakel, W., Habashy, S., Raouf, S.A., Elhefnawi, M., Eladawy, M.I., ElHefnawi, M.: Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients. IEEE\/ACM Trans. Comput. Biol. and Bioinf. 15, 861\u2013868 (2018). https:\/\/doi.org\/10.1109\/TCBB.2017.2690848","journal-title":"IEEE\/ACM Trans. Comput. Biol. and Bioinf."},{"key":"3752_CR26","doi-asserted-by":"crossref","unstructured":"Vats, V., Zhang, L., Chatterjee, S., Ahmed, S., Enziama, E., Tepe, K.: A Comparative Analysis of Unsupervised Machine Techniques for Liver Disease Prediction. In: 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). pp.\u00a0486\u2013489 (2018)","DOI":"10.1109\/ISSPIT.2018.8642650"},{"key":"3752_CR27","doi-asserted-by":"publisher","first-page":"37","DOI":"10.20894\/IJCOA.101.006.001.009","volume":"6","author":"Assistant","year":"2017","unstructured":"Assistant, Professor, Department of Computer Science, Thiruvalluvar University College of Arts and Science, Thennangur, V., Kuppan, P., Manoharan, N.: Head and Assistant Professor, Department of Computer Science, Thiruvalluvar University College of Arts and Science, Thennangur,Vandavasi: A Tentative analysis of Liver Disorder using Data mining Algorithms J48, Decision Table and Naive Bayes. IJCOA. 6, 37\u201340 (2017). https:\/\/doi.org\/10.20894\/IJCOA.101.006.001.009","journal-title":"IJCOA."},{"key":"3752_CR28","doi-asserted-by":"publisher","first-page":"923","DOI":"10.17706\/jsw.12.12.923-933","volume":"12","author":"BZ University","year":"2017","unstructured":"Department of Information Technology, University, B.Z., Pakistan, Pasha, M., Fatima, M.: Comparative Analysis of Meta Learning Algorithms for Liver Disease Detection. JSW. 12, 923\u2013933 (2017). https:\/\/doi.org\/10.17706\/jsw.12.12.923-933","journal-title":"JSW."},{"key":"3752_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.05.276","author":"TR Baitharu","year":"2016","unstructured":"Baitharu, T.R., Pani, S.K.: Procedia Comput. Sci. 85, 862\u2013870 (2016). https:\/\/doi.org\/10.1016\/j.procs.2016.05.276 Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset"},{"key":"3752_CR30","doi-asserted-by":"crossref","unstructured":"Sontakke, S., Lohokare, J., Dani, R.: Diagnosis of liver diseases using machine learning. In: 2017 International Conference on Emerging Trends Innovation in ICT (ICEI). pp.\u00a0129\u2013133 (2017)","DOI":"10.1109\/ETIICT.2017.7977023"},{"key":"3752_CR31","first-page":"39","volume-title":"Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT","author":"G Singh","year":"2022","unstructured":"Singh, G., Agarwal, C., Gupta, S.: Detection of Liver Disease Using Machine Learning Techniques: A Systematic Survey. In: Balas, V.E., Sinha, G.R., Agarwal, B., Sharma, T.K., Dadheech, P., Mahrishi, M. (eds.) Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, pp. 39\u201351. Springer International Publishing, Cham (2022)"},{"key":"3752_CR32","doi-asserted-by":"crossref","unstructured":"Pasha, S.N., Ramesh, D., Mohmmad, S., Kishan, P.N., Sandeep, P.A.: C.H.: Liver disease prediction using ML techniques. Presented at the INTERNATIONAL CONFERENCE ON RESEARCH IN SCIENCES, ENGINEERING & TECHNOLOGY, Warangal, India (2022)","DOI":"10.1063\/5.0081787"},{"key":"3752_CR33","unstructured":"Poonguzharselvi, B.: M.M.A.A.: Prediction of Liver Disease Using Machine Learning Algorithm and Genetic Algorithm.Annals of the Romanian Society for Cell Biology.2347\u20132357(2021)"},{"key":"3752_CR34","unstructured":"Yajurved, J., Prasad, P.S., Km, D.U.: Analysis of Chronic Disease (Liver) Prediction Using Machine Learning.Journal of Positive School Psychology.5489\u20135496(2022)"},{"key":"3752_CR35","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3562951","volume-title":"A Prediction Model of Detecting Liver Diseases in Patients using Logistic Regression of Machine Learning","author":"PSM Keerthana","year":"2020","unstructured":"Keerthana, P.S.M., Phalinkar, N., Mehere, R., Bhanu Prakash Reddy, K., Lal, N.: A Prediction Model of Detecting Liver Diseases in Patients using Logistic Regression of Machine Learning. Social Science Research Network, Rochester, NY (2020)"},{"key":"3752_CR36","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.eswa.2016.08.065","volume":"67","author":"M Abdar","year":"2017","unstructured":"Abdar, M., Zomorodi-Moghadam, M., Das, R., Ting, I.-H.: Performance analysis of classification algorithms on early detection of liver disease. Expert Syst. Appl. 67, 239\u2013251 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2016.08.065","journal-title":"Expert Syst. Appl."},{"key":"3752_CR37","unstructured":"UCI Machine Learning Repository: : Data Set, https:\/\/archive.ics.uci.edu\/ml\/datasets\/ILPD+(Indian+Liver+Patient+Dataset"},{"key":"3752_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2018","unstructured":"Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., Liu, H.: Feature Selection: A Data Perspective. ACM Comput. Surv. 50, 1\u201345 (2018). https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM Comput. Surv."},{"key":"3752_CR39","doi-asserted-by":"publisher","unstructured":"Research, Scholar, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India., Latha, P.H., Mohanasundaram, R., Professor, A., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.: A New Hybrid Strategy for Malware Detection Classification with Multiple Feature Selection Methods and Ensemble Learning Methods. IJEAT. 9, 4013\u20134018 (2019). https:\/\/doi.org\/10.35940\/ijeat.B4666.129219","DOI":"10.35940\/ijeat.B4666.129219"},{"key":"3752_CR40","unstructured":"Bhandari, N.: ExtraTreesClassifier, (2018). https:\/\/medium.com\/@namanbhandari\/extratreesclassifier-8e7fc0502c7,"},{"key":"3752_CR41","unstructured":"ML | Extra Tree Classifier for Feature Selection:, (2019). https:\/\/www.geeksforgeeks.org\/ml-extra-tree-classifier-for-feature-selection\/,"},{"key":"3752_CR42","unstructured":"9 Feature Transformation & Scaling: Techniques| Boost Model Performance, (2020). https:\/\/www.analyticsvidhya.com\/blog\/2020\/07\/types-of-feature-transformation-and-scaling\/,"},{"key":"3752_CR43","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s12859-016-1423-9","volume":"18","author":"M Radovic","year":"2017","unstructured":"Radovic, M., Ghalwash, M., Filipovic, N., Obradovic, Z.: Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinf. 18, 9 (2017). https:\/\/doi.org\/10.1186\/s12859-016-1423-9","journal-title":"BMC Bioinf."},{"key":"3752_CR44","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1007\/s10586-021-03348-7","volume":"25","author":"SN Aslan","year":"2022","unstructured":"Aslan, S.N., \u00d6zalp, R., U\u00e7ar, A., G\u00fczeli\u015f, C.: New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration. Cluster Comput. 25, 1575\u20131590 (2022). https:\/\/doi.org\/10.1007\/s10586-021-03348-7","journal-title":"Cluster Comput."},{"key":"3752_CR45","doi-asserted-by":"publisher","first-page":"10313","DOI":"10.1007\/s11042-022-12200-y","volume":"81","author":"MG Lanjewar","year":"2022","unstructured":"Lanjewar, M.G., Gurav, O.L.: Convolutional Neural Networks based classifications of soil images. Multimed Tools Appl. 81, 10313\u201310336 (2022). https:\/\/doi.org\/10.1007\/s11042-022-12200-y","journal-title":"Multimed Tools Appl."},{"key":"3752_CR46","doi-asserted-by":"publisher","first-page":"16537","DOI":"10.1007\/s11042-022-12392-3","volume":"81","author":"MG Lanjewar","year":"2022","unstructured":"Lanjewar, M.G., Morajkar, P.P., Parab, J.: Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud. Multimed Tools Appl. 81, 16537\u201316562 (2022). https:\/\/doi.org\/10.1007\/s11042-022-12392-3","journal-title":"Multimed Tools Appl."},{"key":"3752_CR47","unstructured":"Brownlee, J.: How to Use StandardScaler and MinMaxScaler Transforms in Python, (2020). https:\/\/machinelearningmastery.com\/standardscaler-and-minmaxscaler-transforms-in-python\/,"},{"key":"3752_CR48","unstructured":"Understanding, L., Regression, (2017). https:\/\/www.geeksforgeeks.org\/understanding-logistic-regression\/,"},{"key":"3752_CR49","doi-asserted-by":"crossref","unstructured":"Lanjewar, M.G., Parate, R.K., Parab, J.S.: Machine Learning Approach with Data Normalization Technique for Early Stage Detection of Hypothyroidism. In: Artificial Intelligence Applications for Health Care, pp. 91\u2013108. CRC Press (2022)","DOI":"10.1201\/9781003241409-5"},{"key":"3752_CR50","unstructured":"Pant, A.: Introduction to Logistic Regression, https:\/\/towardsdatascience.com\/introduction-to-logistic-regression-66248243c148"},{"key":"3752_CR51","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.jestch.2020.12.003","volume":"24","author":"T Karag\u00fcl Y\u0131ld\u0131z","year":"2021","unstructured":"Karag\u00fcl Y\u0131ld\u0131z, T., Yurtay, N., \u00d6ne\u00e7, B.: Classifying anemia types using artificial learning methods. Eng. Sci. Technol. Int. J. 24, 50\u201370 (2021). https:\/\/doi.org\/10.1016\/j.jestch.2020.12.003","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"3752_CR52","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861\u2013874 (2006). https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recognit. Lett."},{"key":"3752_CR53","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10586-018-2036-z","volume":"22","author":"S Chidambaram","year":"2019","unstructured":"Chidambaram, S., Srinivasagan, K.G.: Performance evaluation of support vector machine classification approaches in data mining. Cluster Comput. 22, 189\u2013196 (2019). https:\/\/doi.org\/10.1007\/s10586-018-2036-z","journal-title":"Cluster Comput."},{"key":"3752_CR54","unstructured":"Priya, M., Juliet, P., Tamilselvi, P.: Performance Analysis of Liver Disease Prediction Using Machine Learning Algorithms, https:\/\/www.semanticscholar.org\/paper\/Performance-Analysis-of-Liver-Disease-Prediction-Priya-Juliet\/d5bd2f34087fd9e4de29eb6cff328f7bc5e63b20"},{"key":"3752_CR55","doi-asserted-by":"publisher","first-page":"388","DOI":"10.22214\/ijraset.2018.2056","volume":"6","author":"A Pathan","year":"2018","unstructured":"Pathan, A.: Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder. IJRASET. 6, 388\u2013394 (2018). https:\/\/doi.org\/10.22214\/ijraset.2018.2056","journal-title":"IJRASET."},{"key":"3752_CR56","doi-asserted-by":"publisher","first-page":"323","DOI":"10.14419\/ijet.v7i3.34.19217","volume":"7","author":"S Muthuselvan","year":"2018","unstructured":"Muthuselvan, S., Rajapraksh, S., Somasundaram, K., Karthik, K.: Classification of Liver Patient Dataset Using Machine Learning Algorithms. IJET. 7, 323 (2018). https:\/\/doi.org\/10.14419\/ijet.v7i3.34.19217","journal-title":"IJET."},{"key":"3752_CR57","doi-asserted-by":"publisher","first-page":"e0221476","DOI":"10.1371\/journal.pone.0221476","volume":"14","author":"H Kaur","year":"2019","unstructured":"Kaur, H., Bhalla, S., Raghava, G.P.S.: Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles. PLoS ONE. 14, e0221476 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0221476","journal-title":"PLoS ONE."},{"key":"3752_CR58","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.21533\/pen.v7i3.667","volume":"7","author":"A Shaker Abdalrada","year":"2019","unstructured":"Shaker Abdalrada, A., Hashim Yahya, O., Hadi, M., Alaidi, A., Ali Hussein, N., Alrikabi, T.H., Al-Quraishi, H.: A Predictive model for liver disease progression based on logistic regression algorithm. PEN. 7, 1255 (2019). https:\/\/doi.org\/10.21533\/pen.v7i3.667","journal-title":"PEN."},{"key":"3752_CR59","doi-asserted-by":"publisher","first-page":"554","DOI":"10.17762\/itii.v9i2.382","volume":"9","author":"GS Harshpreet Kaur","year":"2021","unstructured":"Harshpreet Kaur, G.S.: The Diagnosis of Chronic Liver Disease using Machine Learning Techniques. ITII. 9, 554\u2013564 (2021). https:\/\/doi.org\/10.17762\/itii.v9i2.382","journal-title":"ITII."},{"key":"3752_CR60","unstructured":"Dattatreya, P., Mankame, Harshitha, R., Navya, N.C., Nitin Ravichander, Machine Learning Techniques in Analysis and Prediction of Liver Disease,IJIRT,Volume8, Issue 2, (2022)"},{"key":"3752_CR61","doi-asserted-by":"publisher","first-page":"294","DOI":"10.3390\/livers1040023","volume":"1","author":"F Mostafa","year":"2021","unstructured":"Mostafa, F., Hasan, E., Williamson, M., Khan, H.: Statistical Machine Learning Approaches to Liver Disease Prediction. Livers. 1, 294\u2013312 (2021). https:\/\/doi.org\/10.3390\/livers1040023","journal-title":"Livers."},{"key":"3752_CR62","doi-asserted-by":"publisher","first-page":"100255","DOI":"10.1016\/j.imu.2019.100255","volume":"17","author":"JH Joloudari","year":"2019","unstructured":"Joloudari, J.H., Saadatfar, H., Dehzangi, A., Shamshirband, S.: Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection. Inf. Med. Unlocked. 17, 100255 (2019). https:\/\/doi.org\/10.1016\/j.imu.2019.100255","journal-title":"Inf. Med. Unlocked"},{"key":"3752_CR63","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.1016\/j.procs.2020.03.226","volume":"167","author":"J Singh","year":"2020","unstructured":"Singh, J., Bagga, S., Kaur, R.: Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques. Procedia Comput. Sci. 167, 1970\u20131980 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.03.226","journal-title":"Procedia Comput. Sci."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03752-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-022-03752-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03752-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T20:08:25Z","timestamp":1697486905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-022-03752-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"references-count":63,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["3752"],"URL":"https:\/\/doi.org\/10.1007\/s10586-022-03752-7","relation":{"references":[{"id-type":"uri","id":"","asserted-by":"subject"}]},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,13]]},"assertion":[{"value":"10 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"We have no conflicts of interest to disclose","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}}]}}