Diagnosis of Cardiovascular Disease using Deep Learning

Abstract

Abstract: Cardiovascular disease is one of the most horrendous illnesses, particularly the silent heart attack that strikes a person so abruptly that there is no time for treatment. It's difficult to diagnose a disease of this nature. One of the scariest diseases that c an kill a person at any time without warning is heart disease, and most doctors are unable to predict silent heart attacks. The lac k of specialists and an increase in cases of wrong diagnoses have fueled the demand for the creation of an efficient cardiovascul ar disease prediction system. This resulted in the exploration and development of original machine learning and medical data mi ning methodologies. The principal goal of this research is to identify the most crucial qualities for silent heart attack identificatio n by using classification algorithms to extract significant patterns and features from medical data. Although it is not innovative t o build such a system, the current ones have flaws and are not designed to detect the likelihood of silent heart attacks. Another is sue with the present heart attack prediction method is the use of characteristics. Choosing the typical features for the heart attac k prediction algorithm frequently yields unreliable results. To increase prediction accuracy, the suggested method aims to extract suitable attributes from the datasets. We developed a framework in this exploration that can understand the principles of predicti ng the risk profile of patients with the clinical data parameters. This research suggests an effective neural network with convolut ional layers to classify clinical data that is noticeably class-imbalanced. In order to forecast the development of Coronary Heart Disease, data from the National Health and Nutritional Examination Survey (NHANES) is collected (CHD). This research aime d to design a robust deep-learning algorithm to predict heart disease. Heart disease prediction is performed using SMOTE and MLP Classifier algorithms and Deep Neural Network Algorithms. The effectiveness of the model that accurately predicts the pre sence or absence of heart disease was examined using DNN and ANN. In this research article, we'll look at a machine-learnin g model that can clearly assess cardiac issues and be utilized by analysts and medical professionals