Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

• Published in: Engineering reports (Hoboken, N.J.) Vol. 5; no. 12
• Main Authors: Yenurkar, Ganesh Keshaorao, Mal, Sandip, Nyangaresi, Vincent O., Hedau, Anshul, Hatwar, Prajwal, Rajurkar, Shreyas, Khobragade, Juli
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.12.2023; Wiley
• Subjects: Accuracy; Algorithms; Applications programs; Asthma; Coronaviruses; COVID-19; Data analysis; Disease transmission; Epidemics; Health services; Heroku; high‐risk patients; Holidays & special occasions; Hospitals; Hypertension; Illnesses; Machine learning; Medical personnel; Mortality; Neural networks; Pandemics; Patients; Python flask; random forest classifier; Risk; Sex; Supervised learning; XGBoost
• ISSN: 2577-8196; 2577-8196
• DOI: 10.1002/eng2.12678

AI and machine learning are increasingly often applied in the medical industry. The COVID‐19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID‐19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high‐risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre‐processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID‐19 high‐risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not. This paper provides the analysis of COVID‐19 dataset to forecast individuals severity by using machine learning algorithms and smart notification assistant to high‐risk patients.