TY  - JOUR
VL  - 11
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117017701&doi=10.3390%2fapp11188352&partnerID=40&md5=6f0dd8621224863ea4e55747c0ca8ee0
JF  - Applied Sciences (Switzerland)
A1  - Reddy, K.V.V.
A1  - Elamvazuthi, I.
A1  - Aziz, A.A.
A1  - Paramasivam, S.
A1  - Chua, H.N.
A1  - Pranavanand, S.
SN  - 20763417
PB  - MDPI
Y1  - 2021///
ID  - scholars14486
TI  - Heart disease risk prediction using machine learning classifiers with attribute evaluators
IS  - 18
N2  - Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, â??kâ?? in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148 using the full set of attributes and 86.468 was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25 accuracy improvement by tuning the hyperparameter â??kâ?? to 9 with the chi-squared attribute set. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
N1  - cited By 37
AV  - none
ER  -