Coronary Artery Disease Prediction using Data Mining Techniques

The health industry produces a gigantic amount of data and by using this huge data many diseases can be detected and predicted at early stage and can be even cured. Diseases like coronary artery, cancer, and tumour disease cause a big threat to human. In this regard, the proposed research work has been carried out to predict coronary disease and it has also been outlined below. The cardiac illness is often identified by using AI strategies. The medical statistics are taken as input and then those capabilities are modelled for prediction. This model can then be used to envision subsequent medical data. The three different data mining techniques are used in our proposed model. The accuracy of the model using each of the algorithms is intended. Then the one with adequate accuracy is taken as the model for predicting the coronary disease.In this new-fangled era in healthcare domains, generally the statistics mining techniques are used for prediction of diseases. It is very challenging task for the researchers to predict the medical diseases using gigantic medical databases. To overcome from this issue the researchers use various statistics mining techniques for the disease prediction. In our paper, the main intention of the research work is to predict coronary diseases using machine learning algorithms. In this paper, SVM, Naïve Bayes and Random Forest model have been used to predict the heart diseases which will be helpful for the medical sector. Early prediction of coronary illness is very vital to save human life and take proper steps to control the disorder. The main aim fo the proposed research work is to explore the new methods for early prediction of coronary disease by using statistic mining techniques.