Abstract
Cardiovascular diseases are a group of heart and blood vessel disorders that are common worldwide, taking millions of lives each year. Many factors contribute towards a person getting a positive diagnosis such as hypertension, diabetes, and cholesterol level, among others. It proves to be a challenge to accurately predict the presence of a cardiovascular disease as this is a complex task. Timely and correct diagnosis is considered a highly important area in the field of clinical research and healthcare. Vast troves of data are collected by the healthcare industry every year and using this data effectively can prove to be an invaluable asset to the field. Machine learning and ensemble classification models can be employed to accurately predict heart disease in patients. Data mining techniques presented in this paper make use of patient data, from the UCI machine learning repository, to build a classification model that can be applied to new cases for the detection of heart disease. An ensemble model made from random forest and support vector machine algorithms is proposed which reports an accuracy of 89% in detecting heart disease. This model is further compared with other algorithms based on evaluation metrics like precision, recall and F-1 score.
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Acknowledgements
The authors would like to thank Universiti Sains Malaysia (USM) for the support and encouragement to conduct this research through the Research University Grant (RUI) (1001/PKOMP/8014084).
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Kashif, F., Yusof, U.K. (2021). Detection of Cardiovascular Disease Using Ensemble Machine Learning Techniques. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_18
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DOI: https://doi.org/10.1007/978-3-030-70713-2_18
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