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A Comparative Study of Different Machine Learning Tools in Detecting Diabetes

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dc.contributor.author Ghosh, Pronab
dc.contributor.author Azam, , Sami
dc.contributor.author Karim, Asif
dc.contributor.author Hassan, Mehedi
dc.contributor.author Roy, Kuber
dc.contributor.author Jonkman, Mirjam
dc.date.accessioned 2022-03-01T06:36:03Z
dc.date.available 2022-03-01T06:36:03Z
dc.date.issued 2021
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7359
dc.description.abstract A significant proportion of people around the world are currently suffering from the harmful effects of diabetes and a considerable number of them not being identified at an early stage. Over time this may result in serious health problem such as blindness and kidney failure. To accurately classify the disease, different machine learning (ML) approaches can be utilized. In this context, four separate ML algorithms, namely Gradient Boosting (GB), Support Vector Machine (SVM) AdaBoost (AB), and Random Forest (RF) are evaluated using the Pima Indians diabetes dataset, first with based on all features, then to the features selected with the Minimal Redundancy Maximal Relevance (MRMR) Feature Selection (FS) approach. Seven different types of performance evaluation metrics were computed with a 10-fold cross-validation (CV) approach. Computational complexity is also evaluated. The best results were obtained with the Random Forest approach, achieving an accuracy of 99.35%. en_US
dc.language.iso en_US en_US
dc.publisher Scopus en_US
dc.subject MRMR en_US
dc.subject Gradient Boosting en_US
dc.subject Support Vector Machine (RBF kernel) en_US
dc.subject AdaBoost en_US
dc.subject Random Forest en_US
dc.title A Comparative Study of Different Machine Learning Tools in Detecting Diabetes en_US
dc.type Article en_US


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