Machine Learning Based Hybrid Technique for Heart Disease Prediction

There is a lot of information in the medical services industry. With such a big amount of data, the illness can often be identified, predicted, or reduced. Infections such as cardiovascular sickness, malignant development, tumours, and so on pose a significant threat to humanity. In this research, we try to focus on coronary sickness prediction using AI approaches. Coronary illness is frequently predicted. Information such as pulse, hypertension, diabetes, and cigarette smoking is collected, and these highlights are then displayed for forecasting. The calculations like K-nearest neighbor, Random Forest and Decision tree are used. We have also proposed a hybrid model of combining Decision Tree and Random Forest. The precision of the model is to investigate utilizing every one of the calculations. At that point with the more exactness is taken as a result of the model for anticipating the daringness infection.