• Akruti Dave
• Prof. Gayatri Pandi

According to World Health Organization (WHO) , 17.7 Million people die every year due to various Cardiovascular diseases. The prediction of heart disease system can become a boon for the population to predict the health complexity at early stage .Various methods and techniques are undergoing the research phase for developing the most accurate heart disease prediction system. Machine Learning based Artificial Neural Network (ANN) is the most promising one in this area due to its computing functions , neuron functionality and machine learning rules. Various layers in ANN are input, hidden & output layer ,which make the system nearly most accurate for the prediction approach. Existing systems are working well in predicting the accurate result, but still more attributes of data and complexity of health parameters make the root layer for the innovation of new approaches. This study focuses on the analysis of various neural network approaches for the prediction of heart disease. The dataset for experiment used here is Cleveland Heart Disease dataset available on UCI machine learning repository.The proposed model uses a combined approach neural network in which one multilayer perceptron neural network(MLP) is used for prediction and another MLP neural network is used for error reduction with backpropagation approach.Also,the input parameters are the combination of clinical attributes as well as additional attributes.

Artificial Neural Network, Heart Disease Prediction, Combined Approach

"ANALYSIS OF HEART DISEASE PREDICTION SYSTEM USING ARTIFICIAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.336-342, November-2018, Available :http://www.jetir.org/papers/JETIR1811A41.pdf

"ANALYSIS OF HEART DISEASE PREDICTION SYSTEM USING ARTIFICIAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp336-342, November-2018, Available at : http://www.jetir.org/papers/JETIR1811A41.pdf