PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Genomics & Informatics10.5808/gi.2018.16.4.e312018164e31Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy PopulationEun Kyung Choe, Hwanseok Rhee, Seungjae Lee, Eunsoon Shin, Seung-Won Oh, Jong-Eun Lee, Seung Ho Choihttp://genominfo.org/upload/pdf/gi-2018-16-4-e31.pdf, http://genominfo.org/journal/view.php?doi=10.5808/GI.2018.16.4.e31, http://genominfo.org/upload/pdf/gi-2018-16-4-e31.pdf
BMC Public Health10.1186/s12889-022-13131-x2022221Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in KoreaJunho Kim, Sujeong Mun, Siwoo Lee, Kyoungsik Jeong, Younghwa Baekhttps://link.springer.com/content/pdf/10.1186/s12889-022-13131-x.pdf, https://link.springer.com/article/10.1186/s12889-022-13131-x/fulltext.html, https://link.springer.com/content/pdf/10.1186/s12889-022-13131-x.pdf
Diabetes & Metabolic Syndrome: Clinical Research & Reviews10.1016/j.dsx.2021.1022632021155102263Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary dataMd. Maniruzzaman, Md. Merajul Islam, Md. Jahanur Rahman, Md. Al Mehedi Hasan, Jungpil Shinhttps://api.elsevier.com/content/article/PII:S1871402121002836?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1871402121002836?httpAccept=text/plain
Diabetes & Metabolic Syndrome: Clinical Research & Reviews10.1016/j.dsx.2022.1026092022169102609Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic predictionSwati V. Narwane, Sudhir D. Sawarkarhttps://api.elsevier.com/content/article/PII:S1871402122002235?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S1871402122002235?httpAccept=text/plain
Symmetry10.3390/sym120405812020124581Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning AlgorithmsGuadalupe Obdulia Gutiérrez-Esparza, Oscar Infante Vázquez, Maite Vallejo, José Hernández-Torrucohttps://www.mdpi.com/2073-8994/12/4/581/pdf
Diabetology & Metabolic Syndrome10.1186/s13098-022-00969-92022141Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive reviewElaheh Afsaneh, Amin Sharifdini, Hadi Ghazzaghi, Mohadeseh Zarei Ghobadihttps://link.springer.com/content/pdf/10.1186/s13098-022-00969-9.pdf, https://link.springer.com/article/10.1186/s13098-022-00969-9/fulltext.html, https://link.springer.com/content/pdf/10.1186/s13098-022-00969-9.pdf
10.36227/techrxiv.15103602.v12021Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning ModelsJaydip Sen, Sidra Mehtab, Abhishek Duttahttps://ndownloader.figshare.com/files/29037078
10.36227/techrxiv.151036022021Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning ModelsJaydip Sen, Sidra Mehtab, Abhishek Duttahttps://ndownloader.figshare.com/files/29037078
2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)10.1109/icaeee54957.2022.98364402022Prediction of Bank Performance Using Machine Learning and Genetic Algorithm Hybrid ModelsUmmey Hany Ainan, Md. Nur-E-Arefinhttp://xplorestaging.ieee.org/ielx7/9836329/9836341/09836440.pdf?arnumber=9836440
2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)10.1109/ic-etite47903.2020.2882020Potential Breast Cancer Drug Prediction using Machine Learning ModelsN. Priya, G. Shobanahttp://xplorestaging.ieee.org/ielx7/9070069/9077582/09077768.pdf?arnumber=9077768