Research Article
A Hybrid Approach for Heart Disease Prediction
@INPROCEEDINGS{10.4108/eai.7-6-2021.2308784, author={Kumaresan Angappan and N. Meenakshi and Joel Evans E and Harshitha Bharanika and Suganya Jothi}, title={A Hybrid Approach for Heart Disease Prediction}, proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India}, publisher={EAI}, proceedings_a={I3CAC}, year={2021}, month={6}, keywords={data cleaning feature scores machine learning stacked generalization genetic algorithm}, doi={10.4108/eai.7-6-2021.2308784} }- Kumaresan Angappan
N. Meenakshi
Joel Evans E
Harshitha Bharanika
Suganya Jothi
Year: 2021
A Hybrid Approach for Heart Disease Prediction
I3CAC
EAI
DOI: 10.4108/eai.7-6-2021.2308784
Abstract
An estimated count of 17900000people, i.e., 31% of deaths is related to cardiac diseases every year. This number is projected to rise to 22million by the end of this decade, making cardiac diseases among the most common global sources of death. The only effective method is ECG tests among various other limited methods for the detection of heart diseases. With regard to cardiac diseases, early diagnosis has the potential to produce better treatment outcomes. Hence, this paper discusses a Machine Learning methodology to detect heart diseases using the Data Set for Heart Disease by the repository of UCI Machine learning. This system is developed based on classification algorithms such as Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees and Random forest classifiers. We define a hybrid stacking method and genetic algorithm which increases the accuracy achieved by the basic individual data mining techniques of classification.
