A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation.

Abstract

Objective: At present, there is no consensus on the best strategy for interpreting the cardiopulmonary exercise test's (CPET) results. This study is aimed at assessing the potential of using computer-aided algorithms to evaluate CPET data for identifying chronic heart failure (CHF) and chronic obstructive pulmonary disease (COPD).

Methods: Data from 234 CPET files from the Pulmonary Institute, at Sheba Medical Center, and the Givat-Washington College, both in Israel, were selected for this study. The selected CPET files included patients with confirmed primary CHF (n = 73), COPD (n = 75), and healthy subjects (n = 86). Of the 234 CPETs, 150 (50 in each group) tests were used for the support vector machine (SVM) learning stage, and the remaining 84 tests were used for the model validation. The performance of the SVM interpretive module was assessed by comparing its interpretation output with the conventional clinical diagnosis using distribution analysis.

Results: The disease classification results show that the overall predictive power of the proposed interpretive model ranged from 96% to 100%, indicating very high predictive power. Furthermore, the sensitivity, specificity, and overall precision of the proposed interpretive module were 99%, 99%, and 99%, respectively.

Conclusions: The proposed new computer-aided CPET interpretive module was found to be highly sensitive and specific in classifying patients with CHF or COPD, or healthy. Comparable modules may well be applied to additional and larger populations (pathologies and exercise limitations), thereby making this tool powerful and clinically applicable.