Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

• DOI: 10.1371/journal.pone.0179805
• Publication Date: 2017-07-24T17:29:43Z
• Authors: Manal Alghamdi; Mouaz Al-Mallah; Steven Keteyian; Clinton Brawner; Jonathan Ehrman; Sherif Sakr
• Keywords: Male; anzsrc-for: 4611 Machine Learning; Science; anzsrc-for: 46 Information and Computing Sciences; 610; Coronary Artery Disease; Machine Learning; 03 medical and health sciences; 46 Information and Computing Sciences; Risk Factors; 4611 Machine Learning; Machine Learning and Artificial Intelligence; Diabetes Mellitus; Humans; Heart Failure; 0303 health sciences; anzsrc-for: 42 Health Sciences; Prevention; Diabetes; Q; Decision Trees; R; 42 Health Sciences; 3 Good Health and Well Being; Bayes Theorem; 3. Good health; Logistic Models; Physical Fitness; Exercise Test; Medicine; Female; Research Article

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.