The Random Forest classifier trained using the SMOTE sampling technique achieved the best performance for predicting 10-year all-cause mortality using cardiorespiratory fitness data, with an AUC of 0.97.
Cohort (n=34,212)
No
Can machine learning techniques accurately predict 10-year all-cause mortality using cardiorespiratory fitness data in patients without known coronary artery disease or heart failure?
Machine learning models, particularly Random Forest combined with SMOTE sampling, can highly accurately predict 10-year all-cause mortality using cardiorespiratory fitness data.
Effect estimate: AUC 0.97
BACKGROUND: Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). METHODS: We use data of 34,212 patients free of 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 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. RESULTS: Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. CONCLUSIONS: The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.
Sakr et al. (Fri,) conducted a cohort in All-cause mortality risk assessment (n=34,212). Machine learning classification techniques (Random Forest with SMOTE) vs. Other machine learning models and models without SMOTE was evaluated on Prediction of all-cause mortality (AUC 0.97). The Random Forest classifier trained using the SMOTE sampling technique achieved the best performance for predicting 10-year all-cause mortality using cardiorespiratory fitness data, with an AUC of 0.97.