Machine Learning Models for Early Gingival Health Screening Using Routine Clinical and Behavioral Data
DOI:
https://doi.org/10.63158/journalisi.v8i4.1640Keywords:
Gingival Health Screening, Machine Learning, Random Forest, Multiclass Classification, Clinical Decision Support, Periodontal DiseaseAbstract
This study addresses the high prevalence of periodontal disease in Indonesia by developing an accessible, early screening tool that overcomes the limitations of existing AI approaches, which heavily rely on costly radiographic imaging. We developed a machine learning framework utilizing demographic, clinical, behavioral, and medical-history data from 500 patients at the Maninjau Community Health Centre. Four algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost—were evaluated using stratified 5-fold cross-validation and ROC curve assessment. The results demonstrate that Random Forest achieved the highest overall classification performance, attaining an accuracy of 95.2%, an AUC of 0.983, and an F1-score of 0.9521, with superior capability across all categories (Normal, Gingivitis, and Periodontitis). To demonstrate practical applicability, this model was deployed as a Streamlit-based prototype decision-support application for real-time clinical use. This research provides significant value by demonstrating that routinely collected, non-invasive clinical and behavioral data can serve as potent predictors for periodontal screening, eliminating the dependency on expensive imaging. This offers a cost-effective digital health framework suitable for primary healthcare settings. Future research will focus on validating this model using larger, multi-center datasets to ensure broader generalizability.
Downloads
References
[1] M. Hung et al., “Development of a recommender system for dental care using machine learning,” SN Appl. Sci., vol. 1, no. 7, Jul. 2019, doi: 10.1007/s42452-019-0795-7.
[2] D. M. Alalharith et al., “A deep learning-based approach for the detection of early signs of gingivitis in orthodontic patients using faster region-based convolutional neural networks,” Int. J. Environ. Res. Public Health, vol. 17, no. 22, pp. 1–10, 2020, doi: 10.3390/ijerph17228447.
[3] P. N. Papapanou et al., “Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions,” J, Periodontol., vol. 89, December 2017, pp. 173–182, 2018, doi: 10.1002/JPER.17-0721.
[4] J. G. Caton, G. Armitage, T. Berglundh, I. L. C. Chapple, S. Jepsen, and K. S. Kornman., “A new classification scheme for periodontal and peri‐implant diseases and conditions – Introduction and key changes from the 1999 classification,” J. Periodontol., vol. 89, no. S1, Jun. 2018, doi: 10.1002/JPER.18-0157.
[5] P. Wahyuni, A. Rahardjo, and H. Novrinda, “Determinan Status Periodontal pada Remaja di Indonesia: Analisis data Riskesdas 2018 Determinants of Periodontal Status in Adolescents in Indonesia: Analysis of 2018 Riskesdas Data,” Cakradonya Dent. J., 2024.
[6] F. Mashaq, S. Herlina, and P. Latipa, “Expert System To Diagnose Periodontal Disease Using Certainty Factor Method Sistem Pakar untuk Mendiagnosa Penyakit Periodontal Menggunakan Metode Certainty Factor,” J. Komputer, Inf. dan Teknol., vol. 1, no. 1, pp. 134–140, 2021, doi: 10.53697/jkomitek.v1i1.
[7] Muhammad Hilmy Haidar Aly, “Klasifikasi Diabetes Menggunakan Algoritma Support Vector Machine Radial Basis Function,” J. Tek. Inform. dan Teknol. Inf., vol. 4, no. 1, pp. 28–38, Jan. 2024, doi: 10.55606/jutiti.v4i1.3420.
[8] P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “AI in health and medicine,” Nat. Med., vol. 28, no. 1, pp. 31–38, Jan. 2022, doi: 10.1038/s41591-021-01614-0.
[9] M. A. Nazir, “Prevalence of periodontal disease , its association with systemic diseases and prevention,” vol. 1, no. 2, 2017.
[10] B. Li, B. Aljabri, D. Beaton, L. Al-omran, M. A. Hussain, and D. S. Lee, “Predicting outcomes following endovascular aortoiliac revascularization using machine learning,” 2025.
[11] A. A. Alshehri et al., “A decision support system based on classification algorithms for the diagnosis of periodontal disease,” J. Med. Artif. Intell., vol. 8, no. 3, pp. 181–192, 2024, doi: 10.4103/sjoralsci.sjoralsci_50_24.
[12] C. A. Șalgău et al., “Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review,” Ann Biomed Eng 52, 2348–2371 (2024). https://doi.org/10.1007/s10439-024-03559-0.
[13] H. I. Fageeh et al., “Accuracy in Diagnosing Periodontitis Using the AAP / EFP 2017 Classification,” Int. Dent. J., vol. 75, no. 6, p. 103907, 2025, doi: 10.1016/j.identj.2025.103907.
[14] E. Nurlia, M. Jajuli, and I. Purnamasari, “Penerapan Naïve Bayes Untuk Klasifikasi Tingkat Risiko Diagnosis Gigi Di Uptd Puskesmas Cingambul,” J. Inform dan Komputer, vol. 4, no. 2, 2021, doi: 10.33387/jiko.
[15] N. H. Danial and D. Setiawati, “Convolutional Neural Network (Cnn) Based On Artificial Intelligence In Periodontal Diseases Diagnosis,” Interdental J. Kedokt. Gigi, vol. 20, no. 1, pp. 139–148, Apr. 2024, doi: 10.46862/interdental.v20i1.8641.
[16] C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 526–534. doi: 10.1016/j.procs.2021.01.199.
[17] C. Z. V. Junus, T. Tarno, and P. Kartikasari, “Klasifikasi Menggunakan Metode Support Vector Machine Dan Random Forest Untuk Deteksi Awal Risiko Diabetes Melitus,” J. Gaussian, vol. 11, no. 3, pp. 386–396, Jan. 2023, doi: 10.14710/j.gauss.11.3.386-396.
[18] C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artif. Intell. Rev., vol. 54, no. 3, pp. 1937–1967, Mar. 2021, doi: 10.1007/s10462-020-09896-5.
[19] L. A. Celi et al., “Leveraging Data Science for Global Health", Springer Cham, 1st ed., 2020. [E-book] Available : Springer Nature Link.
[20] Y. Altork, “Comparative analysis of machine learning models for wind speed forecasting: Support vector machines, fine tree, and linear regression approaches,” Int. J. Thermofluids, vol. 27, April 2025, p. 101217, 2025, doi: 10.1016/j.ijft.2025.101217.
[21] N. Bhuvaneswari, M. Sowmiya, A. K. Nandhana, and K. Preethi, “A High-Accuracy Heart Disease Prediction Model Using XG-Boost: A Machine Learning Approach for Clinical Decision Support,” in 2026 IEEE Madhya Pradesh Section Conference (MPCON), IEEE, Mar. 2026, pp. 221–225. doi: 10.1109/MPCON69668.2026.11508271.
[22] P. Lestari, I. Tahyudin, A. Tikaningsih, and A. Nurhopipah, “Optimization of the XG-Boost Algorithm for Predicting Stroke Patient Care Outcomes,” Telematika, vol. 18, no. 1, pp. 13–33, 2025, doi: 10.35671/telematika.v18i1.2817
[23] T. Denœux, “Logistic regression, neural networks and Dempster–Shafer theory: A new perspective,” Knowledge-Based Syst., vol. 176, pp. 54–67, Jul. 2019, doi: 10.1016/j.knosys.2019.03.030.
[24] S. B. D. Widyawati, P. Purwadi, and I. R. Yunita, “Comparative Analysis of Random Forest, Logistic Regression and SVM for Stunting Prediction Using Anthropometric Data,” J. Inf. Syst. Informatics, vol. 7, no. 4, pp. 4271–4293, 2025, doi: 10.63158/journalisi.v7i4.1387.
[25] J. H. Lee, D. H. Kim, S. N. Jeong, and S. H. Choi, “Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,” J. Dent., vol. 77, July, pp. 106–111, 2018, doi: 10.1016/j.jdent.2018.07.015.
[26] K. Deng, F. Zonta, H. Yang, G. Pelekos, and M. S. Tonetti, “Development of a machine learning multiclass screening tool for periodontal health status based on non-clinical parameters and salivary biomarkers,” J. Clin. Periodontol., July 2023, pp. 1547–1560, 2023, doi: 10.1111/jcpe.13856.
[27] S. S. Yadadi, R. M. Shetty, and V. Sowmya, “Machine learning assisted classification of periodontal health and periodontitis using alveolar bone loss measurements on bitewing radiographs,” May, 2026, doi: 10.3389/froh.2026.1814025.
[28] G. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. M. Moons, “Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) the TRIPOD statement,” Circulation, vol. 131, no. 2, pp. 211–219, 2015, doi: 10.1161/CIRCULATIONAHA.114.014508.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
