Predictive Modeling for Early Identification of Disease Severity in Acute Respiratory Infections: A Case Study with COVID-19

The COVID-19 pandemic, declared a global emergency by WHO in March 2020, has had a profound impact on global health. With millions affected, the pandemic's magnitude became evident. Even advanced healthcare systems grappled with resource shortages, from protective gear to ventilators. Late diagnosis and the inability to adapt to new SARS-CoV-2 variants led to misdiagnoses and high casualties. Overburdened healthcare facilities and the absence of a centralized patient data system hindered swift decision-making. The lack of real-time monitoring meant many didn't receive timely, personalized care, underscoring the need for an integrated healthcare response during global emergencies. To tackle these challenges and improve hospital resource management during pandemics, this study presents a web approach harnessing big data analytics to develop a machine learning predictive model. This model has been developed to enable hospitals to assess patients' severity in real-time, taking into account a wide range of factors such as symptoms, medical history, and demographics. To acquire data regularly, a streaming data pipeline was established using Apache Kafka and Apache Streaming. In predicting the severity levels of patients, both Multinomial Logistic Regression and Multilayer Perceptron (MLP) models were employed. One noteworthy finding was that using datasets with balanced classes yielded significantly higher accuracy for the MLP model compared to unbalanced datasets. Specifically, MLP achieved the highest accuracy of 60.4% when trained on balanced classes. This model's real-time severity insights have the potential to revolutionize decision-making and resource allocation in healthcare. It represents a significant step towards a more effective response to future pandemics and the strengthening of the global healthcare system.