Newfangled Approach for Early Detection and Prevention of Ischemic Heart Disease using Data Mining
The significant advances in biotechnology and health science have led to produce large amount of data. As the heart disease causing the major problem because it is very difficult to identify disease based on symptoms. For that we need a lot of experience and knowledge. Finding the exact cause of disease can help to patient cure at early stage of risk level. Ischemic heart Disease is one of the most common causes of death nowadays. In this research work, we have applied data mining classification techniques on the stroke dataset and proposed a recommendation system in which a person can come to know their risk level of IHD. By taking proper cure and treatment can help to survive more years if they have high risk of IHD. We have used five data mining algorithms Logistic Regression, Decision Tree, K nearest Neighbor, Naïve Bayes and SVM on Ischemic Stroke Dataset and got the highest accuracy 97.91% using Support Vector Machine algorithm.
Ischemic Heart Disease, CVD, Prediction, Data Mining, Classification, Accuracy