A New Efficiency Improvement of Ensemble Learning for Heart Failure Classification by Least Error Boosting
نویسندگان
چکیده
Heart failure is a very common disease, often silent threat. It's also costly to treat and detect. There steadily higher incidence rate of the disease at present. Although researchers have developed classification algorithms. Cardiovascular data were used by various ensemble learning methods, but efficiency was not high enough due cumulative error that can occur from any weak learner effect accuracy vote-predicted class label. The objective this research development new algorithm improves patients with heart failure. This paper proposes Least Error Boosting (LEBoosting), adaboost.m1's performance for accuracy. finds lowest among learners be identify possible errors update distribution create best final hypothesis in classification. Our trial will use clinical records dataset, which contains 13 features cardiac patients. Performance metrics are measured through precision, recall, f-measure, accuracy, ROC curve. Results experiment found proposed method had compared naïve bayes, k-NN,and decision tree, outperformed other ensembles including bagging, logitBoost, LPBoost, adaboost.m1, an 98.89%, classified capabilities who died accurately as well tree completely indistinguishable. findings study LEBoosting able maximize reductions learner's training process effectiveness cardiology classifiers provide theoretical guidance develop model analysis prediction disease. novelty improve original finding order hypothesis, give highest efficiency. Doi: 10.28991/ESJ-2023-07-01-010 Full Text: PDF
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ژورنال
عنوان ژورنال: Emerging science journal
سال: 2022
ISSN: ['2610-9182']
DOI: https://doi.org/10.28991/esj-2023-07-01-010