• V.Poornima
• Dr. D. Gladia

Data mining techniques have a wide range application in clinical solution support systems for prediction and analysis of various diseases with high accuracy. These practices have an ability to determine the various unseen patterns and deals in medical data and also used in designing clinical support systems. Thus the most important applications of these system are in analyses of heart diseases. Over the world, heart diseases is a major one of the leading causes to death. In every system that forecast the heart diseases uses the clinical dataset and it has various limitations and inputs of many complex tests results. There is no much system which predicts the heart diseases based on Risk Factors (RF) such as family history, age, diabetes, hypertension, high cholesterol, alcohol intake, tobacco smoking, physical inactivity or obesity etc. These visible RFs are commonly observed in all heart disease patients. The system based on the RFs will not only help the medical professionals but it would act as a warning for the presence of heart diseases before he/she goes for costly medical checkups or visits the hospital. Therefore this paper presents a technique for forecasting the heart disease by the use of major RFs. This involves data mining tools, neural networks and Genetic Algorithms (GA) and the system uses the global optimization benefit of GA for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation. The system was implemented in Matlab and predicts the risk of heart disease with an accuracy of 89%

Data mining, Matlab, genetic algorithms, neural networks

"Heart disease prediction using risk factors in genetic neural network based data mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.58-65, February-2019, Available :http://www.jetir.org/papers/JETIR1902C12.pdf

"Heart disease prediction using risk factors in genetic neural network based data mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp58-65, February-2019, Available at : http://www.jetir.org/papers/JETIR1902C12.pdf