Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records of...Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVMare evaluated using MAE, RAE, precision, recall, f-measure, and accuracy.The overall finding indicates that for the dataset taken from UCI, the SVMclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 forMAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for MAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.展开更多
基金the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVMare evaluated using MAE, RAE, precision, recall, f-measure, and accuracy.The overall finding indicates that for the dataset taken from UCI, the SVMclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 forMAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for MAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.
