Complementary Approach of SVM for Heart Disease Prediction
Keywords:
SVM, Machine learning, Algorithm, Precision, Accuracy, Heart DiseaseAbstract
To the point, Coronary-Heart-Disease is in records of killing huge number of lives every year. India contributes to 32% of deaths among all countries. In terms of data, 85% of them resulted from either heart attacks or strokes. In other terms, ⅘ people are risked to heart strokes. Over 75% of CHD deaths occur in low- and middle-income countries, where high blood pressure is one of the most important risk factors for middle age group to the risk of death through CHDs fall in 48 to 70. So, it is a need of time to focus on heart disease and make people in an lively active mode. To predict the accuracy in detection of heart disease we have attempted to describe the popular SVM machine learning along with its own set of strengths which is showed through complementary approach, Confusion Matrix. Our study includes following parts: Introduction, Factor affection heart functioning, Literature Review, SVM as Complementary approach, Methodology, Conclusion and References.
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