Key result
A stacked ensemble machine learning model using five heart rate variability features successfully discriminated between cancer patients and healthy controls with an accuracy of 86.5% and an AUC of 0.945.
Why the study?
Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability compared to healthy controls, but whether machine learning models can discriminate cancer patients using 5-min ECG recordings required evaluation.
Does a machine learning model using HRV features from 5-min ECG recordings accurately discriminate between cancer patients and healthy controls?
Case-Control (n=134)
No
Does a machine learning model using HRV features from 5-min ECG recordings accurately discriminate between cancer patients and healthy controls?
Effect estimate: AUC 0.945 (95% CI 0.8916-0.9993)
Machine learning models using short-term HRV features show high accuracy in distinguishing cancer patients from healthy controls, highlighting the potential of HRV as a marker of autonomic dysfunction in oncology.
HRV-ML models may support autonomic assessment in cancer; hypothesis-generating and requires prospective validation before any clinical use.
Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-ECG recordings. We selected 12 HRV features based on previous research and compared the results between cancer patients and healthy individuals using Wilcoxon sum-rank test. Recursive Feature Elimination (RFE) identified the top five features, averaged over 5 min and employed them as input to three different ML. Next, we created an ensemble model based on a stacking method that aggregated the predictions from all three base classifiers. All HRV features were significantly different between the two groups. SDNN, RMSSD, pNN50%, HRV triangular index, and SD1 were selected by RFE and used as an input to three different ML. All three base-classifiers performed above chance level, RF being the most efficient with a testing accuracy of 83%. The ensemble model showed a classification accuracy of 86% and an AUC of 0.95. The results obtained by ML algorithms suggest HRV parameters could be a reliable input for differentiating between cancer patients and healthy controls. Results should be interpreted in light of some limitations that call for replication studies with larger sample sizes.
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Vigier et al. (2021) conducted a case-control in Cancer (n=134). Machine learning-based heart rate variability (HRV) analysis vs. Healthy controls was evaluated on Classification of cancer patients versus healthy controls using a stacked ensemble machine learning model on the testing set (AUC 0.945, 95% CI 0.8916-0.9993). A stacked ensemble machine learning model using five heart rate variability features successfully discriminated between cancer patients and healthy controls with an accuracy of 86.5% and an AUC of 0.945.
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