Abstract
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.
Highlights
Over the last century, the prevalence of diabetes has been increasing dramatically with the aging population worldwide
The aim of this study is to take advantage of the unique opportunity provided by our access to a large and rich clinical research dataset collected by the The Henry Ford ExercIse Tesing (FIT) project [13] and using it to investigate the relative performance of various machine learning classification methods such as Decision Tree (DT), Naïve Bayes (NB), Logistic Regression (LR), Logistic Model Tree (LMT) and Random Forests (RF) for predicting incident diabetes using medical records of cardiorespiratory fitness
The results show that the Logistic Regression (LR) classifier achieves the highest performance (69.1% for G1 and 68.9% for G2) while the J48 Decision Tree (DT) classifier achieves the lowest performance (63.2% for G1 and 64.5% for G2)
Summary
The aim of this study is to take advantage of the unique opportunity provided by our access to a large and rich clinical research dataset collected by the The Henry Ford ExercIse Tesing (FIT) project [13] and using it to investigate the relative performance of various machine learning classification methods such as Decision Tree (DT), Naïve Bayes (NB), Logistic Regression (LR), Logistic Model Tree (LMT) and Random Forests (RF) for predicting incident diabetes using medical records of cardiorespiratory fitness
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