Classifying diabetes using data mining algorithms

• Published in: AIP conference proceedings Vol. 3153; no. 1
• Main Authors: Bau, Yoon-Teck, Shaifuddin, Nurshara Batrisyia, Lee, Kian-Chin
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 27.06.2024
• Subjects: Algorithms; Classifiers; Data mining; Decision trees; Diabetes; Scientific visualization
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0217308

Across the globe, diabetes is recognized as one of the many causes of deaths, especially in Third World countries as there is a lack of treatment for diabetes, especially in the early stages. In study, the presence of diabetes will be classified within the community, thus contributing to the existing technology within the healthcare system. Our discovery can help doctors to predict the existence of diabetes accurately and alert patients to seek early treatments. Four data mining algorithms were used within this study which consists of both single and ensemble classifiers. The two single classifiers are decision tree, and logistic regression classifier while the ensemble classifiers are random forest, and stacking. These classifiers are chosen as they are efficient and high in performance. This research uses the PIMA diabetes dataset as it can be obtained by the general public. The stratify cross-validation is used to ensure the efficiency of the models. Ensemble classifiers show better or similar testing results compared to single classifiers. From data visualisation, two important features are discovered.