Heart Disease Prediction using Clustered Particle Swarm Optimization Techniques

Heart Disease prediction is one of the important research areas in Health care Management System (HMS). Predicting heart diseases in advance then only we can reduce the life losses. Machine learning techniques are used to identify the heart diseases in advance but till now it not achieved the satisf...

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Bibliographic Details
Published in2022 IEEE 6th Conference on Information and Communication Technology (CICT) pp. 1 - 5
Main Authors Vijaya, J., Rao, Mallikharjuna
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.11.2022
Subjects
Classification algorithms
Clustering algorithms
Heart
Heart disease prediction
Machine learning
Optimization
Particle swarm optimization
Prediction algorithms
Predictive models
Segmentation
Whales
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DOI10.1109/CICT56698.2022.9997925

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Summary:Heart Disease prediction is one of the important research areas in Health care Management System (HMS). Predicting heart diseases in advance then only we can reduce the life losses. Machine learning techniques are used to identify the heart diseases in advance but till now it not achieved the satisfactory result because it is highly depend on the available data. We proposed one novel Clustered Particle Swarm Optimization Techniques (C-PSO) for heart disease prediction. In this process we clustered the data using segmentation techniques then each and every clustered data is considered as optimization classification problem using PSO. We consider the benchmark data set collected from UCI machine learning repository for experimentation. The experimentation result shows that the proposed model achieved the better prediction.
DOI:10.1109/CICT56698.2022.9997925