Using Data Mining Techniques to Enhance Heart Disease Diagnosis

Data mining techniques have been applied magnificently in many fields including science, business, the Web, bioinformatics, and on different types of data such as visual, textual, spatial, real-time and sensor data. Medical data is still information rich but knowledge poor. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. Using single data mining technique in the diagnosis of heart disease has been comprehensively investigated showing acceptable levels of accuracy. The designed system based on Cleveland Heart Disease Dataset, that consists of 13 features are considered as input.The current research being carried out using the data mining techniques to enhance heart disease diagnosis and prediction including decision trees, Naive Bayes classifiers, K-nearest neighbor classification (KNN) and support vector machine (SVM). Results show that NB classifier achieve 87.45% of classification accuracy

Using Data Mining Techniques to Enhance Heart Disease Diagnosis Keywords:

Heart Disease , Data Mining , K-nearest neighbor , Naïve Bayes , Support Vector Machine , Decision Tree