Exploring Machine Learning Algorithms for Predicting Infectious Diseases
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Abstract
This study explored the application of machine learning algorithms in predicting infectious diseases using epidemiological data from Catbalogan City. The research analyzed datasets on diseases such as influenza, dengue, and malaria to identify patterns that could forecast potential outbreaks at the barangay level. Decision Tree, Random Forest, and Gradient Boosted Tree algorithms were tested to determine predictive accuracy. Evaluation metrics including RMSE, R², MSE, and MAE were used to assess model performance. Results showed that Gradient Boosted Trees produced the …
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Cite This Research
SM Pacolor (2025). Exploring Machine Learning Algorithms for Predicting Infectious Diseases. College of Arts and Sciences, Samar State University. Retrieved from https://gov-aideas.ssu.edu.ph/research/exploring-machine-learning-algorithms-for-predicting-infectious-diseases
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Research Information
- Research Type
- Applied Research
- Sector
- Science, Technology & Innovation
- College
- College of Arts and Sciences
- Published
- March 06, 2026
- Last Updated
- March 06, 2026
- Access Level
- Public
- File Size
- 16.1 MB