Prediction of Heart Disease Using Naive Bayes and Particle Swarm Optimization (PSO) Method

Abstract

Health conditions are becoming more prevalent today as a result of hereditary and societal factors. Particularly, heart disease has been increasingly prevalent recently, putting people's lives in danger. Each person's blood pressure, cholesterol, and pulse rate are unique to them. However, medically validated results show that the normal ranges for blood pressure, cholesterol, pulse rate, and heart rate are 120/90, 100-129, 100, 60-100, and 60-100 bpm, respectively. Major vessels range in width from the aortas 25 mm (1 inch) to the capillaries 8 m. The risk level of each individual is estimated in this study using a variety of classification techniques based on variables like age, gender, blood pressure, cholesterol, and pulse rate. The user's disease is predicted via a "Disease Prediction" method based on predictive modelling using the symptoms they offer as input. The system evaluates the user's symptoms as input and outputs the likelihood that the disease will occur. Naive Bayes and particle Swarm optimization (PSO) method used to predict diseases. These methods determine the likelihood of the condition. As a result, 90% of predictions are accurate on average.