Citation

Thirumalaiappan Ramanathan, Thirumalaimuthu and Hossen, Md. Jakir and Sayeed, Md. Shohel (2024) Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease. International Journal on Robotics, Automation and Sciences, 6 (1). pp. 29-35. ISSN 2682-860X

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Abstract

Designing a hybrid or ensemble data mining system appropriate to the application is a research challenge. Heart disease is a life threatening disease that need to be recognized correctly in the starting stage before it becomes more complex. Using artificial intelligence techniques in a hybrid and ensemble architecture can support the prediction of heart disease more effectively based on the given sample cases. This paper proposes a classification system called genetic algorithm-based ensemble classification system (GA-ECS) for the identification of heart disease. As feature selection is the crucial step before applying the data mining techniques, the genetic algorithm is used in GA-ECS to identify the best features in a given dataset. The Cleveland heart disease dataset is used for testing GA-ECS. The performance of GA-ECS is compared with different machine learning classifiers for the prediction of heart disease. GA-ECS showed a promising outcome with an accuracy of 90% for the diagnosis of heart disease.

Item Type:	Article
Uncontrolled Keywords:	ensemble learning, genetic algorithm, AdaBoost, data mining, medical diagnosis
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	06 Sep 2024 00:32
Last Modified:	06 Sep 2024 00:32
URII:	http://shdl.mmu.edu.my/id/eprint/12965

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