Improving the Performance of Physiologic Hot Flash Measures with Support Vector Machines

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Abstract

Hot flashes are experienced by over 70% of menopausal women. Criteria to classify hot flashes from physiologic signals showvariable performance. The primary aimwas to compare conventional criteria to SupportVectorMachines (SVMs), an advanced machine learning method, to classify hot flashes from sternal skin conductance. Thirty women with >3 hot flashes/day underwent laboratory hot flash testing with skin conductancemeasurement.Hot flashes were quantified with conventional (>2 mmho, 30 s) and SVM methods. Conventional methods had poor sensitivity (sensitivity = 50.41, specificity = 51, positive predictive value (PPV) = 50.94, negative predictive value (NPV) = 50.85) in classifying hot flashes, with poorest performance among women with high body mass index or anxiety. SVM models showed improved performance (sensitivity = 50.89, specificity = 50.96, PPV = 50.85, NPV = 50.96). SVM may improve the performance of skin conductance measures of hot flashes.

Citation

Rebecca C. Thurston, Karen A. Matthews, Javier Hernandez Rivera and Fernando de la Torre
Improving the Performance of Physiologic Hot Flash Measures with Support Vector Machines
International Journal of the Society for Psychophysiological Research (SPR), 2009.
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