Scopus Indexed Publications

Paper Details


Title
A comparative study of different machine learning tools in detecting diabetes
Author
Pronab Ghosh, Kuber Roy, Mehedi Hassan,
Email
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%.

    Keywords
    MRMR Gradient Boosting Support Vector Machine (RBF kernel) AdaBoost Random Forest
    Journal or Conference Name
    Procedia Computer Science
    Publication Year
    2021
    Indexing
    scopus