Author(s) :
R. Jeyachandra, R. Jeya Malar, G. Kharmega Sundararaj, T. Geetha, S. Renuka
Article Name :
Heart Disease Prediction using CNN Based on Deep Learning Algorithm
Abstract :
“Heart diseases” are a group of disorders related to the heart. Heart blood flow is hampered by coronary artery disease (CAD), the most frequent kind of heart disease in the United States. A heart attack may be brought on by a decrease in blood flow. Even though there are several heart conditions that may be avoided or managed, heart disease can also occasionally be “silent,” going undiagnosed until a patient exhibits signs that point to a heart attack, heart failure, or an arrhythmia. This study suggests using a deep learning algorithm-based Convolutional Neural Network (CNN) to detect cardiac disease. Heart disease requires careful management due to its complex nature. This project provides accurate and practical deep learning techniques. The K-means clustering technique may be used to identify groups of data items within a dataset. The technique known as particle swarm optimization (PSO) is used to estimate solutions to very challenging or unsolvable numerical minimization and maximization problems. Long Short-Term Memory (LSTM) classifier, a deep learning technology, is employed to categorize the assaults under various scenarios. In addition to greatly enhancing the healthcare system, this project gives medical practitioners access to an invaluable diagnostic tool. Programs written in Python Jupyter are used to carry out this project. CNN has an accuracy of 97.7% and a Recall comparison of 94.17% respectively.
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