Comparison of Proposed Intelligent Systems with Existing Models for Monitoring Heart Diseases

These days, people are preoccupied with their everyday tasks, paying close attention to their work and other obligations while putting their health on the back burner. A growing number of individuals fall ill every day as a result of their hectic lifestyle and disrespect for their health. Hence, the majority of the population across the world affects by heart disease. Heart disease is one of the major causes of death globally, and early detection and monitoring are critical for effective treatment. Moreover, the use of machine learning is increasing quickly all around the world, particularly in the healthcare industry. Therefore, intelligent models can be developed or introduced by using machine learning approaches that predict the health of a patient’s heart from its risk factors. This paper presents a comparative study in which models are proposed using fuzzy logic and hybrid system and also their performances are compared with each other as well as existing models. The comparison is made by considering the classification accuracy of each model. The model which has the highest percentage of accuracy is considered as the most accurate model for the monitoring of heart disease. According to the results, it is found that the neuro fuzzy methodology assists in implementing an intelligent model which has 98.90 percent accuracy of a classification and also outperforms other existing models.