Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

International Journal of Online and Biomedical Engineering

Halima EL Hamdaoui¹  Nour El Houda Chaoui¹  Badr Alami¹  Said Boujraf¹  Mustapha Maaroufi¹ 

[1]University Sidi Mohamed Ben Abdellah, Fez, Morocco

关键词: heart disease, clinical decision systems, machine learning, random forest, adaboost algorithm, uci heart disease dataset.;   

DOI  :  10.3991/ijoe.v17i11.24781

来源: DOAJ

【 摘 要 】

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

【 授权许可】

Unknown