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Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

Angela Pimentel, Hugo Gamboa, Isa Maria Almeida, Pedro Matos, Rogério T. Ribeiro, João Raposo
Copyright: © 2019 |Pages: 20
ISBN13: 9781522571223|ISBN10: 1522571221|EISBN13: 9781522571230
DOI: 10.4018/978-1-5225-7122-3.ch011
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MLA

Pimentel, Angela, et al. "Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus." Chronic Illness and Long-Term Care: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2019, pp. 198-217. https://doi.org/10.4018/978-1-5225-7122-3.ch011

APA

Pimentel, A., Gamboa, H., Almeida, I. M., Matos, P., Ribeiro, R. T., & Raposo, J. (2019). Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus. In I. Management Association (Ed.), Chronic Illness and Long-Term Care: Breakthroughs in Research and Practice (pp. 198-217). IGI Global. https://doi.org/10.4018/978-1-5225-7122-3.ch011

Chicago

Pimentel, Angela, et al. "Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus." In Chronic Illness and Long-Term Care: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 198-217. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7122-3.ch011

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Abstract

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

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