IIARD International Institute of Academic Research and Development

RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY (RJPST )

E-ISSN 2579-0536
P-ISSN 2695-2696
VOL. 8 NO. 6 2025
DOI: 10.56201/rjpst.vol.8.no6.2025.pg29.46

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Intelligent Framework for Classification of Influenza Severity for Respiratory Disease Management

Anthony Edet, Omayeka Chukwuemeke, Helen Godwin, and Ebupntak Eyo

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Abstract

Introduction: Influenza is a highly contagious respiratory illness that poses significant public health risks, with severity varying across individuals. Accurately assessing the severity of influenza is crucial for timely intervention and resource allocation. This study explores the application of machine learning, specifically Random Forest regression, to predict influenza severity using key physiological indicators. Problem Statement: Traditional clinical assessment methods for influenza severity rely heavily on subjective evaluations and may not fully capture complex interactions among multiple health indicators. There is a need for a robust, data-driven approach that can objectively predict severity and inform early clinical decisions. Aim: The primary objective of this research is to develop and evaluate a machine learning model capable of predicting the severity index of influenza based on key physiological features, thereby enabling early detection and improved patient management. Methodology: A comprehensive datasetsourced from Kaggle was used, including featuressuch as body temperature, respiratory rate, oxygen saturation, cough intensity, fatigue level, and white blood cell (WBC) count. After data preprocessing, the dataset was split into training (80%) and testing (20%) sets. The Random Forest regression model was selected for its capacity to handle nonlinear feature interactions and prevent overfitting. Model performance was assessed using Mean Squared Error (MSE) and the coefficient of determination (R²). Feature importance analysis and visualizations, including regression plots and scatter visualizations, were conducted to interpret the model outputs. Results: The Random Forest regression model achieved an MSE of 0.0173 and an R² value of 0.6630, indicating that the model explained approximately 66.3% of the variance in severity index. Key features such as respiratory rate, oxygen saturation, body temperature, and WBC count demon

keywords:

Influenza, Contagious Respiratory Illness, Covid-19, Pneumonia, SVM, Disease

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