Improving CNN predictive accuracy in COVID-19 health analytics

Abstract

The COVID-19 pandemic has underscored the critical necessity for robust and accurate predictive frameworks to bolster global healthcare infrastructures. This study presents a comprehensive examination of convolutional neural networks (CNNs) applied to the prediction of COVID-19-related health outcomes, with an emphasis on core challenges, methodological constraints, and potential remediation strategies. Our investigation targets two principal aims: the identification of COVID-19 infections through chest radiographic imaging, specifically X-rays, and the prognostication of disease severity by integrating clinical parameters and electronic health records. Utilizing a multidimensional dataset encompassing demographic, clinical, and radiological information, we evaluated the efficacy of CNN architectures in forecasting patient prognoses. The CNN model attained an accuracy of 97.2%, reflecting its capability while simultaneously revealing significant challenges, including limited and imbalanced data, as well as difficulties in model interpretability. Principal limitations identified comprise overfitting, data heterogeneity, and the imperative for model adaptability amid evolving SARS-CoV-2 variants. To mitigate these issues, we propose strategies such as data augmentation, transfer learning, ensemble modeling, and enhancements in explainability, which collectively demonstrated improvements in predictive performance and theoretical robustness. This work highlights the imperative for continuous model refinement and the integration of domain-specific expertise to elevate the reliability and applicability of CNN-based prognostic tools for COVID-19 healthcare analytics. Our empirical results indicate that the CNN achieved a precision of 96.8%, an F1-score of 97.2%, and an AUC-ROC of 0.987, underscoring its effectiveness in detecting and classifying COVID-19 cases. These insights provide valuable directions for researchers and clinicians, fostering data-driven approaches in the management and prediction of COVID-19 health outcomes.

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