Early stage prediction of COVID-19 Using machine learning model

The healthcare sector has traditionally been an early use of technological progress and achieved significant advantages, especially in the field machine learning like prediction diseases. COVID-19 epidemic is still having impact on every facet life necessitates a fast accurate diagnosis. Early detection exceptionally critical to saving lives human beings. need for effective, rapid, precise way reduce consultants' workload diagnosing suspected cases emerged. This paper presents proposed model that aims design implement automated predict with high accuracy stages. dataset used this study considers imbalanced converted balanced one using Synthetic Minority Over Sampling Technique (SMOTE). Filter-based feature selection method many algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Logistic Regression, Random Forest (RF) model. Since best classification result was by RF algorithm, algorithm optimized tuning hyperparameters. enhanced from 98.0 99.5.