AN INNOVATIVE REGRESSION-BASED METHOD FOR COVID-19 DETECTION: ENHANCING DIAGNOSTIC PRECISION THROUGH CONTINUOUS PREDICTION

<p style="text-align: justify;">The emergence of the COVID-19 pandemic has underscored the critical importance of accurate and reliable methods for the early detection and management of cases. Traditional approaches to COVID-19 diagnosis often rely on binary classification methods, which may limit their accuracy and robustness. In this study, we propose a novel approach that leverages chest radiography images for predicting COVID-19 cases. By reframing the classification task as a regression problem, we aim to enhance the accuracy and reliability of our predictive model. Our method involves several key steps. Firstly, we collected a dataset of chest radiography images from confirmed COVID-19 cases and non-COVID-19 cases. Next, we preprocessed the images and extracted relevant features using advanced image processing techniques. We then framed the prediction task as a regression problem, allowing us to model the continuous variation in disease severity rather than relying on binary classification. The predictive model was trained using machine learning algorithms, and both internal and external validation were performed to assess its performance. Our method involves converting the classification task into a regression task, which enables improved accuracy and robustness in the model. We performed both internal and external validation, with R train = 0.91, CV-MSE = 0.0253, and Q = 0.91, indicating high accuracy and reliability in predicting COVID-19 cases. Additionally, we conducted an applicability domain analysis, which showed that 99% of unseen data can be accurately predicted by our model. Our findings suggest that our method can be a valuable tool in the early detection and management of COVID-19 cases, which can ultimately improve patient outcomes and public health. Further validation and testing in real-world clinical settings are needed to confirm the effectiveness and generalizability of our approach.</p>