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

Villi Dane M. Go

doi:10.46223/HCMCOUJS.tech.en.13.2.2944.2023

13(2)2023

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

Author - Affiliation:

Villi Dane M. Go - MarinduqueState College

Corresponding author: Villi Dane M. Go - villidanego@gmail.com

DOI:10.46223/HCMCOUJS.tech.en.13.2.2944.2023

Submitted: 29-08-2023
Accepted: 01-10-2023
Published: 31-10-2023

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.

Keywords

communicable disease prediction; disease prediction; early detection; machine learning; public health planning

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Cite this paper as:

Go, V. D. M. (2023). Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models. Ho Chi Minh City Open University Journal of Science – Engineering and Technology, 13(2), 45-54. doi:10.46223/HCMCOUJS.tech.en.13.2.2944.2023

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DOI: https://doi.org/10.46223/HCMCOUJS.tech.en.13.2.2944.2023