An Explainable Clinical Variant Risk Assessment Framework for Genomic Decision Support
DOI:
https://doi.org/10.46880/jmika.Vol10No1.pp350-356Keywords:
Clinical Variant Classification, LightGBM, SHAP, Fuzzy Decision Support System, Precision MedicineAbstract
Clinical variant interpretation is essential for precision medicine; however, conventional machine learning approaches often focus on prediction accuracy without providing sufficient interpretability and decision-support capabilities. This study proposes a hybrid framework integrating Light Gradient Boosting Machine (LightGBM), SHapley Additive exPlanations (SHAP), and a Fuzzy Decision Support System (FDSS) for clinical variant risk assessment using the ClinVar dataset. A stratified sample of 200,000 genetic variants was utilized for model development and evaluation. LightGBM was employed to predict variant pathogenicity, while SHAP was applied to identify feature contributions and improve model transparency. The resulting prediction probabilities were subsequently processed through fuzzy inference to generate interpretable risk categories and recommendation-oriented outputs. Experimental results showed that the proposed framework achieved an Accuracy of 95.89%, Precision of 95.58%, Recall of 82.97%, F1-Score of 88.83%, and ROC-AUC of 98.73%. Explainability analysis revealed that variant-type representation was the most influential predictor of pathogenicity. The proposed framework extends conventional classification by transforming predictive outputs into actionable risk assessments, thereby enhancing transparency and supporting informed genomic decision-making. These findings demonstrate the potential of integrating predictive analytics, explainable artificial intelligence, and fuzzy reasoning for clinical variant assessment in precision medicine.
References
Alshboul, O., Shehadeh, A., Almasabha, G., & Almuflih, A. S. (2022). Extreme gradient boosting-based machine learning approach for green building cost prediction. Sustainability, 14(11), 6651.
Amiri, Z. (2024). Leveraging AI-enabled information systems for healthcare management. Journal of Computer Information Systems, 1–28.
Aregbesola, G. D., Asghar, I., Akbar, S., & Ullah, R. (2025). Fuzzy Logic Model for Informed Decision-Making in Risk Assessment During Software Design. Systems, 13(9), 825.
Bahmane, K., Bhattacharya, S., & Kassem, M. A. (2026). PathoPredictor: A Machine Learning Framework for Predicting Pathogenic Missense Variants in the Human Genome. Journal of Genome Biotechnology and Genetics, 1(1), 3.
Barbitoff, Y. A., Ushakov, M. O., Lazareva, T. E., Nasykhova, Y. A., Glotov, A. S., & Predeus, A. V. (2024). Bioinformatics of germline variant discovery for rare disease diagnostics: current approaches and remaining challenges. Briefings in Bioinformatics, 25(2), bbad508.
Dhanka, S., Sharma, A., Kumar, A., Maini, S., & Vundavilli, H. (2026). Advancements in hybrid machine learning models for biomedical disease classification using integration of hyperparameter-tuning and feature selection methodologies: A comprehensive review. Archives of Computational Methods in Engineering, 33(1), 289–324.
Divya, N., Desai, B. S., Prem, D. S., Bindu, C., Vybhavi, G. Y., Sundaray, M., & Hussein, L. (2025). Advancing genetic variant classification: Integrating machine learning with population genetics and clinical interpretation. AIP Conference Proceedings, 3361(1), 050059.
Izhari, F. I., & Meiyanti, R. (2025). Hybrid Ensemble Learning to Improve Prediction Disease Kidney Chronic. JADEN: Journal of Algorithmic Digital Engineering and Networks, 1(1), 39–46.
Jiang, P. H. W., Wang, W. Y. C., Goh, T., & Hsieh, C.-C. (2024). System integration framework for implementing a machine learning-driven clinical decision support system in emergency departments. Proceedings of the 2024 8th International Conference on Medical and Health Informatics, 120–126.
Kostopoulos, G., Davrazos, G., & Kotsiantis, S. (2024). Explainable artificial intelligence-based decision support systems: A recent review. Electronics, 13(14), 2842.
Kruschel, S., Hambauer, N., Weinzierl, S., Zilker, S., Kraus, M., & Zschech, P. (2026). Challenging the performance-interpretability trade-off: an evaluation of interpretable machine learning models. Business & Information Systems Engineering, 68(1), 159–183.
Makumbura, R. K., Mampitiya, L., Rathnayake, N., Meddage, D. P. P., Henna, S., Dang, T. L., Hoshino, Y., & Rathnayake, U. (2024). Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature. Results in Engineering, 23, 102831.
Méndez-Vidal, C., Bravo-Gil, N., Pérez-Florido, J., Marcos-Luque, I., Fernández, R. M., Fernández-Rueda, J. L., González-del Pozo, M., Martín-Sánchez, M., Fernández-Suárez, E., & Mena, M. (2025). A genomic strategy for precision medicine in rare diseases: integrating customized algorithms into clinical practice. Journal of Translational Medicine, 23(1), 86.
Qiuqian, W., GaoMin, KeZhu, Z., & Chenchen. (2025). A light gradient boosting machine learning-based approach for predicting clinical data breast cancer. Multiscale and Multidisciplinary Modeling, Experiments and Design, 8(1), 75.
Santos, M. R., Guedes, A., & Sanchez-Gendriz, I. (2024). SHapley additive explanations (SHAP) for efficient feature selection in rolling bearing fault diagnosis. Machine Learning and Knowledge Extraction, 6(1), 316–341.
Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., Thakare, R. P., Banday, S., Mishra, A. K., & Das, G. (2023). Next-generation sequencing technology: current trends and advancements. Biology, 12(7), 997.
Srivastava, A., Bhanot, D., Jasim, L. H., Varshney, N., & Patil, V. (2025). Advancements in fuzzy logic applications for diagnostic decision support systems in healthcare. Fuzzy Information and Engineering, 17(3), 284–297.
Tursunalieva, A., Alexander, D. L. J., Dunne, R., Li, J., Riera, L., & Zhao, Y. (2024). Making sense of machine learning: A review of interpretation techniques and their applications. Applied Sciences, 14(2), 496.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Fahmi Izhari, Hanna Willa Dhany, Syarif Hidayat Matondang, Rizki Khairani Nasution
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
