Boonkrong P.Shakya S.Yang J.Simmachan T.Mahidol University2025-10-122025-10-122025-01-01Engineering Proceedings Vol.108 No.1 (2025)https://repository.li.mahidol.ac.th/handle/123456789/112511We developed a machine learning model for disease classification based on the International Classification of Diseases, 10th Revision (ICD-10) codes, analyzing male and female groups using seven features. The three most prevalent ICD-10 classes covered over 98% of the data. Features were selected using the least absolute shrinkage and selection operator, ridge, and elastic net, followed by the mean decrease in accuracy and impurity. A random forest classifier with five-fold cross-validation showed improved performance with more features. Using Shapley additive explanations, age, BMI, respiratory rate, and body temperature were identified as key predictors, with gender-specific variations. Integrating gender-specific insights into predictive modeling supports personalized medicine and enhances early diagnosis and healthcare resource allocation.EngineeringArticleSCOPUS10.3390/engproc20251080262-s2.0-10501784489526734591