Key result
Support vector machine classification of heart rate variability spectral features achieved a maximum accuracy of 100% in distinguishing breast cancer subjects from healthy controls.
Why the study?
Early decision-making in the diagnosis of breast cancer is necessary to decrease mortality.
Does heart rate variability spectral analysis accurately classify breast cancer compared to healthy controls?
Population
114 breast cancer subjects and 13 age-matched healthy controls
Comparison
114 breast cancer subjects vs 13 age-matched healthy controls
Authors
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SVM HRV classification may aid breast cancer detection; leaves open validation in prospective cohorts.
Case-Control (n=127)
Does heart rate variability spectral analysis accurately classify breast cancer compared to healthy controls?
Spectral features of heart rate variability analyzed via support vector machine can classify breast cancer with high accuracy, potentially aiding in early diagnosis and severity assessment.
Shukla et al. (2021) conducted a case-control in Breast cancer (n=127). Spectral features of heart rate variability (HRV) vs. Healthy controls was evaluated on Classification accuracy of spectral measures. Support vector machine classification of heart rate variability spectral features achieved a maximum accuracy of 100% in distinguishing breast cancer subjects from healthy controls.
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