Early Detection of Type 2 Diabetes Using C4.5 Decision Tree Algorithm on Clinical Health Records

Abstract

Type 2 Diabetes is a chronic metabolic disorder marked by elevated blood glucose levels. It is the most prevalent form of diabetes in society, commonly triggered by poor lifestyle habits and hereditary factors. If left unmanaged, the disease can lead to serious complications such as hypertension and other chronic conditions. Therefore, early detection plays a critical role in minimizing long-term impacts and promoting healthier behavioral changes. This research focuses on classifying Type 2 Diabetes using clinical data with the C4.5 Decision Tree algorithm. The dataset encompasses attributes including gender, age, height, weight, waist circumference, BMI, systolic and diastolic blood pressure, respiratory rate, and pulse rate. The model was evaluated under two scenarios: without data balancing and after applying the SMOTE technique for balancing. In the first scenario, the best performance was achieved with a training-testing split of 80:20, resulting in an F1 Score of 67.76%. However, the performance varied across different data proportions. In contrast, the second scenario showed more consistent results, with the 60:40 split yielding the highest F1 Score of 66.67%. These findings suggest that SMOTE effectively reduces bias toward the majority class and enhances sensitivity to the minority class. Therefore, data balancing is a crucial step in developing a reliable classification model for Diabetes Mellitus diagnosis.