International Journal of Science, Engineering and TechnologyAuthors: Mayank Chauhan, Parul Tyagi, Jaishree Goyal
Abstract: Rapid identification of patients at risk of developing severe illness is necessary due to the major challenge presented by the COVID-19 pandemic to global healthcare systems. The purpose of this research is to propose a severity classification system that uses machine learning to predict the clinical outcome of COVID-19 cases at the time of diagnosis or early hospitalization. The study utilizes a structured dataset named “Covid Data.csv” comprising a wide range of demographic, clinical, and comorbidity-related features, such as age, sex, presence of pneumonia, diabetes, hypertension, obesity, ICU admission, and intubation status. Severity levels in terms of patient condition are reflected by the target attribute, 'CLASIFFICATION_FINAL'. In the data preprocessing phase, it was necessary to remove irrelevant records, add missing values, encode categorical features with Label Encoding, and normalize feature distributions by scaling continuous variables. Multi-class classification of severity categories was performed by a Random Forest classifier after preprocessing. Robust predictive performance with high accuracy, precision, and recall across all classes was demonstrated by the model. To assess misclassification patterns and validate reliability, a confusion matrix and classification report were created. Furthermore, the interpretation of the most significant predictors of severity was provided by feature importance analysis. StreamLight was used to integrate the trained model into a web-based user interface to ensure accessibility and usability. Healthcare professionals or users can use this application to input patient data through a form-like interface and receive predicted severity classification instantly, which can improve triage decisions and support early medical interventions.
DOI: http://doi.org/10.5281/zenodo.20339169
