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Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus
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Author(s): Angela Pimentel (FCT-UNL, Portugal), Hugo Gamboa (FCT-UNL, Portugal), Isa Maria Almeida (APDP-ERC, Portugal), Pedro Matos (APDP-ERC, Portugal), Rogério T. Ribeiro (APDP-ERC, Portugal)and João Raposo (APDP-ERC, Portugal)
Copyright: 2017
Pages: 24
Source title:
Ubiquitous Machine Learning and Its Applications
Source Author(s)/Editor(s): Pradeep Kumar (Maulana Azad National Urdu University, India)and Arvind Tiwari (DIT University, India)
DOI: 10.4018/978-1-5225-2545-5.ch005
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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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