Heart disease diagnosis is considered as one of the complicated tasks in medical field. In order to perform heart disease diagnosis an accurate and efficient automation system can be very helpful. In this research, we propose a classifier ensemble method to improve the decision of the classifiers for heart disease diagnosis. Homogeneous ensemble is applied for heart disease classification and finally results are optimized by using Genetic algorithm. Data is evaluated by using 10-fold cross validation and performance of the system is evaluated by classifiers accuracy, sensitivity and specificity to check the feasibility of our system. Comparison of our methodology with existing ensemble technique has shown considerable improvements in terms of classification accuracy.

Fida, B., Nazir, M., Naveed, N., Akram, S. (2011). Heart disease classification ensemble optimization using Genetic algorithm [10.1109/INMIC.2011.6151471].

Heart disease classification ensemble optimization using Genetic algorithm

FIDA, BENISH;
2011-01-01

Abstract

Heart disease diagnosis is considered as one of the complicated tasks in medical field. In order to perform heart disease diagnosis an accurate and efficient automation system can be very helpful. In this research, we propose a classifier ensemble method to improve the decision of the classifiers for heart disease diagnosis. Homogeneous ensemble is applied for heart disease classification and finally results are optimized by using Genetic algorithm. Data is evaluated by using 10-fold cross validation and performance of the system is evaluated by classifiers accuracy, sensitivity and specificity to check the feasibility of our system. Comparison of our methodology with existing ensemble technique has shown considerable improvements in terms of classification accuracy.
Fida, B., Nazir, M., Naveed, N., Akram, S. (2011). Heart disease classification ensemble optimization using Genetic algorithm [10.1109/INMIC.2011.6151471].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/188575
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