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Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.<\/jats:p>","DOI":"10.1186\/s42492-021-00097-7","type":"journal-article","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T14:02:42Z","timestamp":1638540162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":103,"title":["A comprehensive review of machine learning techniques on diabetes detection"],"prefix":"10.1186","volume":"4","author":[{"given":"Toshita","family":"Sharma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manan","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,3]]},"reference":[{"issue":"11","key":"97_CR1","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1007\/s00535-013-0911-1","volume":"49","author":"T Nakahara","year":"2013","unstructured":"Nakahara T, Hyogo H, Yoneda M, Sumida Y, Sumida Y, Fujii H et al (2013) Type 2 diabetes mellitus is associated with the fibrosis severity in patients with nonalcoholic fatty liver disease in a large retrospective cohort of Japanese patients. 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