Prediction of Human Heart Disease
Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases because it is one of the leading causes of deaths all over the world. Almost all systems that predict heart diseases use clinical dataset having parameters and inputs from complex tests conducted in labs. None of the system predicts heart diseases based on risk factors such as age, blood pressure, fasting blood sugar, chest pain etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. System based on such risk factors would not only help medical professionals, but it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical check-ups. Hence this study presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, support vector machine and principle component analysis. The hybrid system implemented uses the global optimization advantage pca for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation.
Keywords: PCA (Principal Component Analysis), SVM (Support Vector Machine), Framingham Risk Score (FRS)
Cite this Article
Nithyashree S, Karthik S. Prediction of Human Heart Disease. Journal of Software Engineering Tools & Technology Trends. 2019; 6(2): 27–31p.
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