Integrating Statistical Methods and Machine Learning Techniques to Analyze and Classify COVID-19 Symptom Severity

Abstract

Background/Objectives: The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), led to significant global health challenges, including the urgent need for accurate symptom severity prediction aimed at optimizing treatment. While machine learning (ML) and deep learning (DL) models have shown promise in predicting COVID-19 severity using imaging and clinical data, there is limited research utilizing comprehensive tabular symptom datasets. This study aims to address this gap by leveraging a detailed symptom dataset to develop robust models for categorizing COVID-19 symptom severity, thereby enhancing clinical decision making. Methods: A unique tabular dataset was created using questionnaire responses from 5654 individuals, including demographic information, comorbidities, travel history, and medical data. Both unsupervised and supervised ML techniques were employed, including k-means clustering to categorize symptom severity into mild, moderate, and severe clusters. In addition, classification models, namely, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), random forest, and a deep neural network (DNN) were used to predict symptom severity levels. Feature importance was analyzed using the random forest model for its robustness with high-dimensional data and ability to capture complex non-linear relationships, and statistical significance was evaluated through ANOVA and Chi-square tests. Results: Our study showed that fatigue, joint pain, and headache were the most important features in predicting severity. SVM, AdaBoost, and random forest achieved an accuracy of 94%, while XGBoost achieved an accuracy of 96%. DNN showed robust performance in handling complex patterns with 98% accuracy. In terms of precision and recall metrics, both the XGBoost and DNN models demonstrated robust performance, particularly for the moderate class. XGBoost recorded 98% precision and 97% recall, while DNN achieved 99% precision and recall. The clustering approach improved classification accuracy by reducing noise and dimensionality. Statistical tests confirmed the significance of additional features like Body Mass Index (BMI), age, and dominant variant type. Conclusions: Integrating symptom data with advanced ML models offers a promising approach for accurate COVID-19 severity classification. This method provides a reliable tool for healthcare professionals to optimize patient care and resource management, particularly in managing COVID-19 and potential future pandemics. Future work should focus on incorporating imaging and clinical data to further enhance model accuracy and clinical applicability.