Deep Learning Techniques for Data Analysis Prediction in the Prevention of Heart Attacks

Abstract

Coronary illness may have an awful scope of divergent conditions that could influence your heart. It is one of the furthermost mind-boggling disorders to foresee a given number of components in your body that can prompt it. Distinguishing and anticipating it represents a lot of challenge for both specialists and analysts. If we extricate the bereavement caused between the avoidable death and the other death in India, the unnecessary death stands in the 3rd position due to Heart-attack. Across the globe, these types of deaths can increase to more than 23.6 million by 2030. Around 80% of deaths by heart attack mainly affects the younger generation. This research article not only supports doctor specialists in detecting and foreseeing heart disease by attaining precision levels but also advises the effective medicine for the disease. Given sensor data, the deep learning algorithms are applied along with Neural Network, Random Forest and Decision Tree classifier for analyzing the patient's data to predict heart disease. The experiment shows that prediction of heart disease gives promising results of about 90% accuracy.