Implementasi dan Evaluasi Performa Algoritma Naïve Bayes dalam Deteksi Dini Penyakit Diabetes
DOI:
https://doi.org/10.47065/bulletincsr.v6i4.1159Keywords:
Diabetes Mellitus; Naïve Bayes; Classification; Machine Learning; Early DetectionAbstract
Diabetes mellitus is one of the most prevalent chronic diseases worldwide and requires early detection to reduce the risk of severe complications through timely intervention. This study aims to implement and evaluate the performance of the Naïve Bayes algorithm in supporting the early detection of diabetes based on patients' health data. The study employed the Pima Indians Diabetes Dataset, consisting of 768 patient records with eight input attributes and one output attribute. During the preprocessing stage, zero values in physiological attributes were treated as missing values and replaced using the median of each respective attribute, followed by data consistency checking and dataset partitioning using the 80:20 split validation method. Model performance was evaluated using a confusion matrix with four performance metrics: accuracy, precision, recall, and F1-score. The experimental results showed that the Naïve Bayes algorithm achieved an accuracy of 88.31%, precision of 87.80%, recall of 90.00%, and an F1-score of 88.89%. These findings indicate that the proposed model performs well in classifying diabetes risk. The implementation of the model in a web-based application is expected to assist healthcare professionals and the general public as an early screening tool to support preliminary decision-making before comprehensive medical examination.
Downloads
References
D. J. Magliano and E. J. Boyko, IDF Diabetes Atlas, 11th ed. Brussels, Belgium: International Diabetes Federation, 2025. [Online]. Available: https://diabetesatlas.org
N. A. ElSayed et al., "Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2025," Diabetes Care, vol. 48, no. Supplement_1, pp. S27–S49, 2025, doi: 10.2337/dc25-S002.
M. Kiran, Y. Xie, N. Anjum, G. Ball, B. Pierscionek, and D. Russell, "Machine Learning and Artificial Intelligence in Type 2 Diabetes Prediction: A Comprehensive 33-Year Bibliometric and Literature Analysis," Frontiers in Digital Health, vol. 7, 2025, doi: 10.3389/fdgth.2025.1557467.
P. D. Petridis, A. S. Kristo, A. K. Sikalidis, and I. K. Kitsas, "A Review on Trending Machine Learning Techniques for Type 2 Diabetes Mellitus Management," Informatics, vol. 11, no. 4, Art. no. 70, 2024, doi: 10.3390/informatics11040070.
S. Afolabi, N. Ajadi, A. Jimoh, and I. Adenekan, "Predicting Diabetes Using Supervised Machine Learning Algorithms on E-Health Records," Informatics and Health, vol. 2, no. 1, pp. 9–16, 2025, doi: 10.1016/j.infoh.2024.12.002.
Y. Resti, E. S. Kresnawati, N. R. Dewi, D. A. Zayanti, and N. Eliyati, "Diagnosis of Diabetes Mellitus in Women of Reproductive Age Using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression," Science and Technology Indonesia, vol. 6, no. 2, pp. 96–104, 2021, doi: 10.26554/sti.2021.6.2.96-104.
L. N. Salsabila, M. R. Dwi Pangga, S. M. Yasser, N. A. Riyani, S. Aminah, and W. Wahyunengsih, "Application of Naïve Bayes Algorithm for Diabetes Prediction," Unisda Journal of Mathematics and Computer Science (UJMC), vol. 10, no. 1, pp. 56–68, 2024, doi: 10.52166/ujmc.v10i1.6886.
L. H. I. Amal et al., "Performance Analysis of Naive Bayes Method for Diabetes Diagnosis," International Journal of Healthcare and Information Technology, vol. 3, no. 2, pp. 78–87, 2025, doi: 10.25047/ijhitech.v3i2.6670.
M. Z. Islam, "Enhancing Diabetes Prediction Accuracy Using Stacked Machine Learning and Deep Learning Models: A Public Health Approach," The Indonesian Journal of Computer Science, vol. 14, no. 4, 2025, doi: 10.33022/ijcs.v14i4.4947.
H. V. Nguyen, Y. Choi, and H. Byeon, "An Explainable Hybrid Deep Learning Model for Prediabetes Prediction in Men Aged 30 and Above," Journal of Men's Health, vol. 20, no. 10, Art. no. 166, 2024, doi: 10.22514/jomh.2024.166.
S. Majyambere, T. Lindgren, C. Twizere, and I. Ntakirutimana, "Early Type 2 Diabetes Risk Prediction Using Explainable Machine Learning in a Two-Stage Approach," Frontiers in Digital Health, vol. 8, 2026, doi: 10.3389/fdgth.2026.1743619.
M. S. Alzboon, M. Alqaraleh, and M. S. Al-Batah, "Diabetes Prediction and Management Using Machine Learning Approaches," Data and Metadata, vol. 4, Art. no. 545, 2025, doi: 10.56294/dm2025545.
D. S. Khafaga, A. H. Alharbi, I. Mohamed, and K. M. Hosny, "An Integrated Classification and Association Rule Technique for Early-Stage Diabetes Risk Prediction," Healthcare, vol. 10, no. 10, Art. no. 2070, 2022, doi: 10.3390/healthcare10102070.
A. H. Mousa et al., "Diabetes at a Glance: Assessing AI Strategies for Early Diabetes Detection and Intervention via a Mobile App," Mesopotamian Journal of Computer Science, vol. 2025, pp. 288–301, 2025, doi: 10.58496/MJCSC/2025/018.
C. C. Olisah, L. Smith, and M. Smith, "Diabetes Mellitus Prediction and Diagnosis from a Data Preprocessing and Machine Learning Perspective," Computer Methods and Programs in Biomedicine, vol. 220, Art. no. 106773, 2022, doi: 10.1016/j.cmpb.2022.106773.
A. M. AbdulAbbas, R. Alkanany, Y. A. K. Al-Nuaimi, and Z. M. A. Al-Hamdawee, "A Sequential Data Preprocessing Pipeline for Diabetes Prediction: A Data Leakage Prevention and Dual-Validation Approach," Engineering, Technology & Applied Science Research, vol. 15, no. 6, pp. 30059–30066, 2025, doi: 10.48084/etasr.14155.
V. Jain, S. Shukla, and N. Khare, "Analysis of Various Data Imputation Techniques for Diabetes Classification on PIMA Dataset," in 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), Piscataway, NJ, USA: IEEE, 2024, pp. 1–6, doi: 10.1109/SCEECS61402.2024.10482050.
V. Jaiswal, A. Negi, and T. Pal, "A Review on Current Advances in Machine Learning Based Diabetes Prediction," Primary Care Diabetes, vol. 15, no. 3, pp. 435–443, 2021, doi: 10.1016/j.pcd.2021.02.005.
K. B. Lesmana and I K. G. Suhartana, "Deteksi Penyakit Diabetes Menggunakan Gaussian Naive Bayes, Regresi Logistik, dan Random Forest," Jurnal Nasional Teknologi Informasi dan Aplikasinya, vol. 1, no. 4, pp. 1209–1214, 2023, doi: 10.24843/JNATIA.2023.v01.i04.p25.
Y. Mao et al., "Value of Machine Learning Algorithms for Predicting Diabetes Risk: A Subset Analysis from a Real-World Retrospective Cohort Study," Journal of Diabetes Investigation, vol. 14, no. 2, pp. 309–320, 2023, doi: 10.1111/jdi.13937.
S. A. Hicks et al., "On Evaluation Metrics for Medical Applications of Artificial Intelligence," Scientific Reports, vol. 12, no. 1, Art. no. 5979, 2022, doi: 10.1038/s41598-022-09954-8.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi dan Evaluasi Performa Algoritma Naïve Bayes dalam Deteksi Dini Penyakit Diabetes
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Nurhasanah Nurhasanah, Nilovar Asyiah, Rahmawati Rahmawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
