Musri Iskandar Nasution (1)
This study aims to develop a risk prediction model for Diabetes Mellitus by applying the Decision Tree C4.5 algorithm using the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach. The dataset used includes data on patients diagnosed and undiagnosed with diabetes, with several important medical attributes such as glucose levels, blood pressure, body mass index, age, and family history [1]. Of these attributes, glucose levels have been shown to be the most dominant factor in distinguishing patients at risk from those without [2]. The data was divided into two parts: 80% for model training and 20% for testing. The evaluation results showed that the model produced an accuracy of 79.3%, a precision of 81.0%, and a recall of 76.5% [3]. This indicates that the model is quite effective in identifying patients at risk of Diabetes Mellitus. However, further optimization, such as attribute enrichment and advanced data processing, is still needed to improve the reliability of the predictive model [4]. The resulting model is expected to be a tool in supporting medical decision making, especially in early diagnosis and preventive measures against diabetes [5]. This approach can also encourage increased public awareness of the importance of regular health monitoring.
Afroz, S., Hossain, M. N., & Rahman, M. A. (2020). DIABETES PREDICTION MODEL USING DATA MINING TECHNIQUES. Informatics in Medicine Unlocked, 21, 100392.
Al-Wajih, E., Abdulkarim, A., & Alshammari, N. (2022). EARLY PREDICTION OF DIABETES BY APPLYING DATA MINING TECHNIQUES: A RETROSPECTIVE COHORT STUDY. Journal of Personalized Medicine, 9(7), 1045.
Aprillia, A., Rohimah, L., & Chodidjah, C. (2024). PREDIKSI DIABETES MENGGUNAKAN ALGORITMA K-NEAREST (KNN) TEKNIK SMOTE-ENN. Infotek: Jurnal Informatika dan Teknologi, 7(2), 234-241.
Bashir, S., Qamar, U., & Khan, F. H. (2023). DIABETES RISK PREDICTION MODEL BASED ON COMMUNITY FOLLOW-UP DATA USING MACHINE LEARNING. Preventive Medicine Reports, 34, 102256.
Chen, L., Zhang, Y., & Wang, J. (2025). MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN TYPE 2 DIABETES PREDICTION: A COMPREHENSIVE BIBLIOMETRIC ANALYSIS. Frontiers in Digital Health, 7, 1557467.
Fathurrahman, I., Nurhidayati, N., & Nur, A. M. (2023). PREDIKSI DIABETES MENGGUNAKAN ALGORITMA NAIVE BAYES DAN GREEDY FORWARD SELECTION. Jurnal Nasional Teknologi dan Sistem Informasi, 9(3), 187-196.
Harsa, H., Pratiwi, H., & Wibawa, A. D. (2021). IMPLEMENTASI ALGORITMA C4.5 UNTUK PREDIKSI PENYAKIT DIABETES MELLITUS. Jurnal Teknologi Informasi dan Ilmu Komputer, 8(4), 745-752.
Iyer, A., Jeyalatha, S., & Sumbaly, R. (2022). MACHINE LEARNING MODELS FOR DATA-DRIVEN PREDICTION OF DIABETES BY LIFESTYLE TYPE. International Journal of Environmental Research and Public Health, 19(21), 14384.
Kadir, T. A., Glebauskiene, B., & Liosis, N. (2020). ANALYSIS AND PREDICTION OF DIABETES COMPLICATION DISEASE USING DATA MINING ALGORITHM. Procedia Computer Science, 167, 1123-1129.
Kusuma, A. W., Sari, R. P., & Widodo, S. (2023). PENERAPAN METODE MACHINE LEARNING UNTUK PREDIKSI DIABETES: STUDI KASUS PADA DATASET PIMA INDIANS DIABETES. Jurnal Sistem Informasi dan Komputer Terapan Indonesia, 5(2), 123-132.
Mujahid, A., Rustam, F., Alvarez, R., Luis Vidal Mazón, J., Díez, I. D. L. T., & Ashraf, I. (2024). PREDICTION OF DIABETES USING DATA MINING AND MACHINE LEARNING ALGORITHMS: A CROSS-SECTIONAL STUDY. Heliyon, 10(4), e25641.
Purnamasari, D., & Wijaya, A. (2021). PREDIKSI RISIKO DIABETES MELLITUS MENGGUNAKAN DECISION TREE C4.5 DAN RANDOM FOREST. Jurnal Informatika dan Komputer, 26(3), 156-165.
Rahman, M. S., & Islam, M. M. (2020). DIABETES PREDICTION USING MACHINE LEARNING ALGORITHMS. Procedia Computer Science, 167, 1130-1137.
Sisodia, D., & Sisodia, D. S. (2021). PREDICTION OF DIABETES USING CLASSIFICATION ALGORITHMS WITH FEATURE SELECTION TECHNIQUES. International Journal of Cognitive Computing in Engineering, 2, 85-90.
Wulandari, S., Putri, N. A., & Hidayat, R. (2022). SISTEM PREDIKSI DIABETES MELLITUS MENGGUNAKAN METODE CRISP-DM DAN ALGORITMA DECISION TREE. Jurnal Teknologi Informasi dan Terapan, 8(1), 34-42.