Evaluation of Machine Learning Models for Classifying Diabetes and Hypertension Using Patient Data from Public Health Center X
DOI:
https://doi.org/10.30871/jaic.v10i3.12736Keywords:
Diabetes Mellitus, Hypertension, Machine Learning, Ensemble learning, ClassificationAbstract
Major non-communicable diseases such as hypertension and diabetes mellitus continue to increase and require accurate and rapid early detection. This study aimed to evaluate the performance of several machine learning algorithms in classifying diabetes mellitus and hypertension using patient medical record data from a public health center in Bandar Lampung City. The dataset consisted of 662 patient records including age, gender, body mass index, blood glucose level, and blood pressure variables. The research stages included exploratory data analysis, data preprocessing, outlier handling using Interquartile Range (IQR), normalization using StandardScaler, and data splitting scenarios of 90:10, 80:20, and 70:30. A 10-fold cross validation approach was also applied to improve model validity. The algorithms tested were Random Forest, Support Vector Machine (SVM), Decision Tree, AdaBoost Classifier, and XGBoost Classifier. Evaluation results using accuracy, recall, precision, f1-score, and confusion matrix showed that Decision Tree achieved the best performance with 95% accuracy in the 80:20 scenario. The best model was implemented on the Streamlit platform and evaluated using the System Usability Scale (SUS) involving 23 respondents, obtaining an average score of 73.48 categorized as acceptable and good. The findings indicate that machine learning has the potential to support early screening and healthcare decision-making.
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