Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models

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ID: 277902
2023
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Abstract
Effective prediction and surveillance of communicable diseases are vital for public health management. This study leveraged machine learning algorithms to predict disease occurrences in the Province of Marinduque, focusing on Hand Foot Mouth Disease, Dengue, Typhoid, Influenza, Chikungunya, Rabies, Measles, Meningitis, Hepatitis, and Acute Bloody Diarrhea using data from 2015 to 2019. The monthly morbidity rate served as the criterion variable. Machine learning models, including Random Forest, Logistic Regression, SVM, and k-Nearest Neighbors, were employed. Material and methods encompassed data collection, preprocessing, feature selection, and model evaluation. Results revealed Random Forest as the most accurate algorithm, with implications for proactive disease management and resource allocation. This research enhances disease prediction methodologies and contributes to public health surveillance.
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go2023communicableho Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Go, Villi Dane M.;
Journal Ho Chi Minh City Open University Journal of Science - Engineering and Technology
Year 2023
DOI
DOI not found
URL
https://journalofscience.ou.edu.vn/index.php/tech-en/article/view/2944
Keywords
Education Biology (General) Biotechnology geography (general) general works social sciences philology. linguistics language and literature theory and practice of education religions. mythology. rationalism

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