Performance evaluation of optimal feature selection-based machine learning for heart disease diagnosis

Today, heart disease is one of the main causes of morbidity and mortality. Since the initial and early diagnosis of this disease is very important and vital and the usual methods used in the medical industry need to spend a lot of time and money to diagnose this disease, accurate prediction of this disease has become a challenge. According to the huge amount of hospital data, which is added to its volume every day, the importance of data mining, which is one of the important techniques for discovering knowledge and hidden patterns, is increasing. Many studies have been done based on data mining to predict heart disease; according to their solution, each one pursues goals such as increasing speed, increasing accuracy, reducing the volume of calculations, and error coefficient. This research aims to increase the reliability and accuracy of heart disease diagnosis using the feature selection technique by meta-heuristic algorithms to extract useful features and to reduce the computational burden, and we use machine-learning algorithms to evaluate the proposed method. The results indicate that the proposed system can diagnose individuals with cardiovascular disease with relatively high levels of accuracy and precision. By improving the efficiency and accuracy of heart disease diagnosis, this research may contribute to better patient outcomes and reduced healthcare costs.