Year: 2024
Type: article
Abstract: A. Diabetes mellitus is a chronic metabolic disease characterized by high blood sugar levels due to insulin production problems or insulin resistance. Early identification of diabetes is crucial for preventing associated complications and effectively managing the condition. This study explores the application of four machine learning models, i.e., K-Nearest Neighbor (KNN), XGBoost, Support Vector Machine (SVM) and Logistic Regression (LR) in diabetes prediction. the main goal is to assess and contrast these models' efficacy in identifying diabetes risk, thereby helping healthcare professionals make timely diagnostic and treatment decisions. The results show that the logistic regression model with an AUC value of 0.95 performs much better than the other models, demonstrating excellent sensitivity and specificity in diabetes identification. The XGBoost model also demonstrates considerable predictive accuracy with an AUC value of 0.84, highlighting its ability to effectively handle large-scale datasets. Although the SVM and KNN models had slightly lower AUC values of 0.79, they still provided reliable predictive capabilities. These results demonstrate how machine learning may be used to improve diabetes prediction.
Author: Shu Yang
Institution: Hong Kong Polytechnic University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4404249728