Prediction of Heart Disease Using Hybrid Naïve Bayes Technique

The domain of medical diagnosis has attracted many researchers. Several cases of human early mortality have been predicted by investigating the diseases. Different reasons are the cause of several diseases and one such cause is heart disease. Set of disorders in the heart lead to heart disease. It consists of blood vessel issues such as irregular heart beat problems, weak heart muscle problems, congenital heart difficulties, cardio vascular defects and coronary artery disorder. Many researchers have summarized idiosyncratic procedures to preserve human life and help health care experts to recognize, prevent and manage heart disorder. Machine Learning is utilized for decision making in many domains. Naive Bayes technique is one approach which uses conditional probability. In this paper we have taken UCI machine learning data repository including of patients affected from heart disease is analyzed using Complement Naive Bayes probability methodology along with correlated features of the heart disease data set. The prediction is done using this concept as hybrid Naive Bayes probability technique. The advantage of Complement Naive Bayes probability prediction is that, it can be used for imbalanced data set. Accuracy of classification of patients suffering from heart disease is predicted. Implementation is done in Python environment.