An efficient stacking based NSGA-II approach for predicting type 2 diabetes

• Published in: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering Vol. 13; no. 1; p. 1015
• Main Authors: Patil, Ratna Nitin, Rawandale, Shitalkumar, Rawandale, Nirmalkumar, Rawandale, Ujjwala, Patil, Shrishti
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.02.2023
• Subjects: Data mining; Decision trees; Diabetes; Diabetes mellitus; Genetic algorithms; Lead time; Machine learning; Prediction models; Public health; Risk analysis; Sorting algorithms; Stacking; Subspace methods
• ISSN: 2088-8708; 2722-256X; 2722-2578; 2722-2578; 2088-8708
• DOI: 10.11591/ijece.v13i1.pp1015-1023

Diabetes has been acknowledged as a well-known risk factor for renal and cardiovascular disorders, cardiac stroke and leads to a lot of morbidity in the society. Reducing the disease prevalence in the community will provide substantial benefits to the community and lessen the burden on the public health care system. So far, to detect the disease innumerable data mining approaches have been used. These days, incorporation of machine learning is conducive for the construction of a faster, accurate and reliable model. Several methods based on ensemble classifiers are being used by researchers for the prediction of diabetes. The proposed framework of prediction of diabetes mellitus employs an approach called stacking based ensemble using non-dominated sorting genetic algorithm (NSGA-II) scheme. The primary objective of the work is to develop a more accurate prediction model that reduces the lead time i.e., the time between the onset of diabetes and clinical diagnosis. Proposed NSGA-II stacking approach has been compared with Boosting, Bagging, Random Forest and Random Subspace method. The performance of Stacking approach has eclipsed the other conventional ensemble methods. It has been noted that k-nearest neighbors (KNN) gives a better performance over decision tree as a stacking combiner.