COMPARISON OF DIABETES DISEASE CLASSIFICATION MODELS USING LOGISTIC REGRESSION AND RANDOM FOREST ALGORITHMS

Abstract

Diabetes is a lifelong chronic disease that disrupts blood sugar regulation. Diabetes is a life-threatening condition that, if left untreated, can lead to death and other health problems. Several medical tests, including the glycated hemoglobin (A1C) test, blood sugar test, oral glucose tolerance test, and fasting blood sugar test, can be used to detect diabetes. According to statistics, high glucose levels are one of the problems associated with diabetes. This study aims to categorize patients into diabetic and non-diabetic groups using specific diagnostic metrics included in the dataset. 1500 patient records with 9 attributes and 2 classes were used by the researchers. The study used machine learning techniques, including Logistic Regression and Random Forest, along with Confusion Matrix and Receiver Operating Characteristics (ROC) assessment. The Random Forest method produced results of 97% accuracy, 97% precision, 100% recall, and 98% f1-score, indicating that the accuracy level seems good but can still be improved. Based on the accuracy findings, Random Forest is the most effective strategy of Logistic Regression.