Advanced prediction of Heart diseases using Artificial Neural Network and Genetic Algorithm

As per the statistics stated in the year 2019 by WHO (World Health Organization), about 70% of deaths among the people aged above 40 years is due to heart attack. Main reason behind this is lack of earlier prediction of heart attacks. In order to improve the country's health care sector, it is a basic necessary to predict the heart attacks earlier and prevent deaths among the individuals. Prediction of heart related issues is done with the help of ECG (Electrocardiogram) data and also the clinical data. Here, the data is to be fed for non linear prediction of the disease. This monitoring module has to be made available for detecting atrial, myocardial, bradycardia, tachycardia, and fibrillation. An efficient method has been leveraged for acquiring clinical data and ECG data is proposed for training the neural network and accurately diagnose the functioning of heart and predict the abnormalities. The proposed work is divided into three steps. In the first step, the ECG of the patient is collected using the 3 led pre-jelled electrodes [2], [18]. ECG which is acquired is processed then filtered and amplified for removing noise. Ana log signal is then converted into the digital one. In the second step, heart rate(HR), FBS (fasting blood sugar), MAP(Mean Arterial Pressure), cholesterol(CH) along with gender is collected. Finally neural network is used for training and testing purpose.