Keywords
C4.5 Algorithm
Diabetes Mellitus
Orange Application
Risk Factors
Abstract
This study aims to analyze the risk factors for diabetes mellitus and build a predictive model using the C4.5 algorithm implemented through the Orange application. The dataset used is the Pima Indians Diabetes Dataset from the UCI Machine Learning Repository containing 768 samples with eight predictor variables and one target variable. The analysis process includes data preprocessing, splitting training and testing data (80:20), building a decision tree model, and evaluating performance using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the C4.5 model achieved an accuracy value of 71.9% and an AUC of 0.674, indicating a fairly good classification ability. The most influential factors on diabetes risk are blood glucose levels, body mass index (BMI), and age. This study contributes to the development of a data mining-based disease prediction model that is easy to implement and can support the decision-making process in the early detection of diabetes mellitus.
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