COVID-19 Disease Prediction System Using Genetic Algorithm (Case Study: Nigeria)

This research paper presents a COVID-19 disease prediction system designed specifically for Nigeria. The system combines the Genetic Algorithm (GA) approach with Support Vector Regression (SVR) models to improve the accuracy and efficiency of COVID-19 prediction models. The main objective is to enable early detection and effective management of COVID-19 cases. By integrating GA and SVR, the system optimizes feature selection and utilizes advanced regression techniques to forecast the likelihood of COVID-19 infection more accurately. The study utilizes a diverse dataset comprising COVID-19 cases reported in Nigeria. The dataset includes variables such as age, gender, comorbidities, symptoms, and geographic location. The effectiveness of the genetic algorithm-based prediction system is initially evaluated through rigorous assessment and comparison. Then, the SVR model is introduced as an alternative approach, and its performance is evaluated based on accuracy, sensitivity, and specificity. The results show that the genetic algorithm-based system achieves an impressive 100 percent accuracy in predicting the likelihood of COVID-19 infection. Upon integrating the SVR model, the accuracy slightly decreases to 97 percent. However, the SVR model demonstrates strong performance and exhibits promising potential for accurate prediction and early detection of COVID-19 cases. In conclusion, the COVID-19 disease prediction system developed using a combination of genetic algorithms and SVR models is a valuable tool for accurately predicting the likelihood of COVID-19 infection in Nigeria.