198 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 11 DOI: 10.4018/978-1-5225-7122-3.ch011 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 prog- nostic 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. Coronary Heart Disease Prognosis Using Machine- Learning Techniques on Patients With Type 2 Diabetes Mellitus 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 João Raposo APDP-ERC, Portugal