COVID-19 health data prediction: a critical evaluation of CNN-based approaches.

The COVID-19 pandemic has significantly accelerated the demand for accurate and efficient prediction models to support effective disease management, containment strategies, and informed decision-making. Predictive models capable of analyzing complex health data are essential for monitoring disease trends, evaluating risk factors, and optimizing resource allocation during the pandemic. Among various machine learning approaches, convolutional neural networks (CNNs) have emerged as powerful tools due to their ability to process large volumes of high-dimensional health data, such as medical images, time-series data, and patient demographics, with impressive precision. This research seeks to systematically examine the challenges and limitations inherent in utilizing CNNs for COVID-19 health data prediction, offering a comprehensive perspective grounded in data science research. Key areas of investigation include issues related to data quality and availability, such as incomplete, noisy, and imbalanced datasets, which often hinder the training of robust models. Additionally, architectural constraints of CNNs, including their sensitivity to hyperparameter tuning and reliance on substantial computational resources, are explored as critical bottlenecks that impact scalability and efficiency. A significant focus is placed on generalization challenges, where models trained on specific datasets struggle to adapt to unseen data from diverse populations or clinical settings, limiting their applicability in real-world scenarios. The study further highlights a reported accuracy of 63%, underscoring the need for improved methodologies to enhance model performance and reliability. By addressing these challenges, this research aims to provide actionable insights and practical recommendations to optimize the use of CNNs for COVID-19 health data prediction. In particular, the study emphasizes the importance of incorporating advanced strategies such as transfer learning, data augmentation, and regularization techniques to overcome dataset limitations and enhance model robustness. The integration of multimodal approaches combining medical images with auxiliary data, such as patient demographics and laboratory results, is proposed to improve contextual understanding and diagnostic precision. Finally, the research underscores the necessity of interdisciplinary collaboration, leveraging domain expertise from data scientists, healthcare professionals, and epidemiologists to develop holistic solutions for tackling the complexities of COVID-19 prediction. By shedding light on the limitations and potential of CNNs in this domain, this study aims to guide researchers and practitioners in making informed decisions about model design, implementation, and optimization. Ultimately, it contributes to advancing AI-driven diagnostics and predictive modeling for COVID-19 and other public health crises, fostering the development of scalable and reliable tools for better healthcare outcomes.