Diagnosis of heart disease is a challenging task which requires much knowledge and experience. The most traditional way for predicting heart disease are doctor’s examinations or taking number of medical tests like as Heart MRI, ECG, Stress Test etc. Now a days, health care industry includes large amount of health care data, which is having hidden medical information. For providing a better and efficient result, novel techniques like Support Vector Machine (SVM) and Sobel Edge Detection has been proposed. This proposed technique provides better output for heart disease detection. The pre-processing step improves the image quality of heart disease MRI image. Increasing of image quality makes the process ease to find affected region. The region of interest techniques sharps the edges in scanned image. Region classification is being applied for isolating the abnormal and normal regions in the heart cells with SVM for identification of various types of abnormalities. The training process classifies the features and recognizes the affected region. The Eclipse IDE tool being used for analyzing the heart disease and several type of heart disease image dataset is being collected from various online sources and stored in a database.

Heart Disease, Support Vector Machine (SVM), Water Shed Segmentation (WSS), Sobel Edge Detection (SED), ROI segmentation, Eclipse IDE, Heart MRI

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