Comparative Analysis of Statistical and Machine Learning Models for Diabetes Prediction Using Healthcare Data - Esha Raffie, B (International Journal of Engineering and Management Research, 2026)
Diabetes mellitus is a chronic metabolic disorder that has become a major global health challenge. Early identification of high-risk individuals is essential for preventing severe complications and reducing healthcare costs. This study aims to identify significant risk factors associated with diabetes and evaluate the predictive performance of statistical and machine learning models using healthcare data. The dataset consists of 2,768 patient records with clinical and demographic variables including pregnancies, glucose, blood pressure, skin thickness, insulin, body mass index (BMI), diabetes pedigree function, and age. Descriptive statistics, correlation analysis, logistic regression, and Random Forest classification were employed. Logistic regression identified glucose, BMI, age, pregnancies, and diabetes pedigree function as significant predictors of diabetes. The Random Forest model achieved superior predictive performance compared to logistic regression. Feature importance analysis indicated that glucose level was the most influential predictor. The findings demonstrate the effectiveness of machine learning techniques for diabetes risk prediction and healthcare decision support.