Dynamic Heart Disease Prediction using Multi-Machine Learning Techniques

Health Care Field having enormous data, for processing these data we must use any advanced techniques which will be helpful to provide the effective results and making effective decisions on data and getting the appropriate results. Heart disease is the leading problem and one of the biggest causes for no. of deaths happening all over the world. In this paper, an effective Heart Disease Prediction framework is implemented using algorithms in Machine Learning such as Gaussian Naive Bayes, Random Forest, K-Nearest Neighbour, Support Vector Machine, Xg-Boost. The framework uses 13 features such as age, gender, blood pressure, cholesterol, obesity, cp, etc. It is a user-friendly system where we are having some phases.In the first phase, we upload the dataset file and select the algorithm to perform on the selected dataset. Then the accuracy is predicted for each selected algorithm along with a graph, and the modal is generated for the one having highest frequency by training the dataset to it. In the next phase, input for each parameter of the heart is given and based on that modal generated, the diseased stage of the heart gets predicted. We then take the precautions based on the condition of the patient. Our strategy is effective in foreseeing the heart illness of a victim. The Heart Disease Prediction Framework progressed in this view is a one of a unique methodology that might be utilized inside the class of heart disease.