Classification of Infectious and Parasitic Diseases by Smart Healthcare System †
1
Issued Date
2025-01-01
Resource Type
eISSN
26734591
Scopus ID
2-s2.0-105017844595
Journal Title
Engineering Proceedings
Volume
108
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Engineering Proceedings Vol.108 No.1 (2025)
Suggested Citation
Yang J., Simmachan T., Shakya S., Boonkrong P. Classification of Infectious and Parasitic Diseases by Smart Healthcare System †. Engineering Proceedings Vol.108 No.1 (2025). doi:10.3390/engproc2025108014 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112524
Title
Classification of Infectious and Parasitic Diseases by Smart Healthcare System †
Author(s)
Corresponding Author(s)
Other Contributor(s)
Abstract
We developed a machine-learning model for the International Classification of Diseases, 10th Revision (ICD-10) classification using data from 5108 patients. Nine features, including age, gender, BMI, and vital signs, were extracted to classify the top three ICD-10 categories: intestinal infections, tuberculosis, and other bacterial diseases. Decision trees, random forest, and XGBoost models were tested using the synthetic minority over-sampling technique (SMOTE) and class weights to minimize class imbalance. Five-fold cross-validation was used using the training and testing datasets in a data ratio of 80:20. The random forest model with class weights showed the best performance. Shapley additive explanations (SHAP) analysis highlighted body-mass index (BMI), gender, and pulse as key features. The developed model showed potential for enhancing ICD-10 classification through real-time and personalized medical applications.