Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

• Published in: PloS one Vol. 19; no. 5; p. e0302595
• Main Authors: Idris, Nur Farahaina, Ismail, Mohd Arfian, Jaya, Mohd Izham Mohd, Ibrahim, Ashraf Osman, Abulfaraj, Anas W., Binzagr, Faisal
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.05.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biology and Life Sciences; Cardiovascular disease; Care and treatment; Classification; Computer and Information Sciences; Data mining; Data Mining - methods; Datasets; Decision trees; Diabetes; Diabetes mellitus; Diabetes Mellitus - diagnosis; Diabetes therapy; Diagnosis; Ecology and Environmental Sciences; Engineering and Technology; Feature selection; Forests; Gestational diabetes; Health care; Health care industry; Humans; Innovations; Machine Learning; Male; Medicine and Health Sciences; Metabolic disorders; Methods; Research and Analysis Methods; Stacking; Support Vector Machine; Support vector machines; Technology application; Time measurement; Type 2 diabetes; India
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302595

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.