A Hybrid Approach for Heart Disease Prediction

Angappan, Kumaresan and Meenakshi, N. and E, Joel Evans and Bharanika, Harshitha and Jothi, Suganya (2021) A Hybrid Approach for Heart Disease Prediction. In: I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: data cleaning feature scores machine learning stacked generalization genetic algorithm
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 11 Jun 2021 08:03
Last Modified: 11 Jun 2021 08:03
URI: https://eprints.eudl.eu/id/eprint/3875

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