Efficient Heart Disease Prediction Using PSO Optimized LSTM Classifier

Efficient Heart Disease Prediction Using PSO Optimized LSTM Classifier

Publication Date : 2023-11-09
Author(s) :

A. Tamizharasi, S. Mohanalakshmi

           
Article Name :

Efficient Heart Disease Prediction Using PSO Optimized LSTM Classifier

Abstract :

Accurate diagnosis and prognosis of cardiovascular disease are essential in the medical field, enabling cardiologists to administer appropriate treatments. Machine learning has gained prominence in the healthcare sector due to its ability to identify patterns in data. Leveraging machine learning for classifying cardiovascular disease occurrences, significantly minimize misdiagnosis. This research focuses on developing a robust model capable of accurately predicting cardiovascular diseases, ultimately aiming to decrease the mortality rates associated with these conditions. To find clusters of data objects in a dataset, K-means clustering algorithm is implemented. The technique known as particle swarm optimization (PSO) is used to estimate solutions to extremely challenging or unsolvable numerical minimization and maximization problems. LSTM classifier, a deep learning technology is employed to categorize the attacks under various scenarios. In addition to greatly enhancing the healthcare system, this research gives medical practitioners access to an invaluable diagnostic tool. The outcomes of proposed work is examined using programs written in Python Jupyter software tool.

No. of Downloads :

6