Heart Disease is the one of the major cause of death especially in developed countries. Some of its types include Arrhythmia, Stroke, High Blood pressure, Cardiac Arrest etc. Thus to help clinicians for early diagnose disease related conditions, some medical decision support system are also designed. Data mining plays an essential role in analyzing huge amount of data. These quick predicting techniques helps medical practitioners to analyze the same. Classification is the most common Machine Learning algorithm used to classify the disease/non-disease patient. In this paper we will analyze and predict the occurrence of heart disease by applying some of the machine learning algorithms like K-Nearest Neighbor , Decision Trees , Random Forest , Adaptive boosting, SVM and Logistic Regression. It will help physicians to estimate the risk in different age groups. The dataset used is taken from Heart Disease database of UCI Machine Learning Datasets. Factors like blood pressure, heart rate, sugar level, cholesterol, age, gender etc. highly affects the result of the algorithm. The accuracy has been improved by working on high-contributing attributes found using feature importance technique.

Heart Disease, Predictive Analysis, Data Mining, SVM, Classification, Decision Tree

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