TITLE

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

AUTHOR(S)
Ul Haq, Amin; Jian Ping Li; Memon, Muhammad Hammad; Nazir, Shah; Sun, Ruinan
PUB. DATE
December 2018
SOURCE
Mobile Information Systems;12/2/2018, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. 0e diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews' correlation coefficient, and execution time. 0e proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. 0e performance of the proposed system has been validated on full features and on a reduced set of features. 0e features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. 0e proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.
ACCESSION #
133398789

 

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