Enhancing COVID-19 Patient Outcomes and Resource Allocation Efficiency Through the Application of a Recursive Classification Model

Abstract: Amid the escalating global mortality stemming from the COVID-19 virus, researchers are dedicated to exploring technological innovations to bolster the efforts of healthcare professionals. Machine Learning techniques are being harnessed to swiftly and accurately predict disease severity in patients with comorbidities, thereby assisting healthcare providers in their evaluations. Presently, initial detection of comorbid patients dataset 273 patients. So basically in the patients dataset the parameters we have are Unnamed, Sex, BP_high, BP_low, RR, Temp, SpO2, Covid, Age. The models used for this project are lasso logistic model which is used for regression model can predict COVID-19 outcomes using clinical data. It identifies key factors for prognosis and avoids overfitting. Researchers use metrics and feature analysis to assess its effectiveness. This approach helps develop data-driven tools for personalized medicine in COVID-19 patients. And Artificial neural networks can analyze COVID-19 data to predict patient outcomes. They learn from patient details to personalize care and support clinical decisions. Challenges include choosing the right data, designing the model, and making it work for new patients. Careful planning is needed for reliable ANN models in COVID-19 research.