Risk Factors in Males and Females for Disease Classification Based on International Classification of Diseases, 10th Revision Codes †
Issued Date
2025-01-01
Resource Type
eISSN
26734591
Scopus ID
2-s2.0-105017844895
Journal Title
Engineering Proceedings
Volume
108
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Engineering Proceedings Vol.108 No.1 (2025)
Suggested Citation
Boonkrong P., Shakya S., Yang J., Simmachan T. Risk Factors in Males and Females for Disease Classification Based on International Classification of Diseases, 10th Revision Codes †. Engineering Proceedings Vol.108 No.1 (2025). doi:10.3390/engproc2025108026 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112511
Title
Risk Factors in Males and Females for Disease Classification Based on International Classification of Diseases, 10th Revision Codes †
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Corresponding Author(s)
Other Contributor(s)
Abstract
We 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.