Improving the performance of physiologic hot flash measures with support vector machines.

Hot flashes are experienced by over 70% of menopausal women. Criteria to classify hot flashes from physiologic signals show variable performance. The primary aim was to compare conventional criteria to Support Vector Machines (SVMs), an advanced machine learning method, to classify hot flashes from sternal skin conductance. Thirty women with > or =4 hot flashes/day underwent laboratory hot flash testing with skin conductance measurement. Hot flashes were quantified with conventional (> or =2 micromho, 30 s) and SVM methods. Conventional methods had poor sensitivity (sensitivity=0.41, specificity=1, positive predictive value (PPV)=0.94, negative predictive value (NPV)=0.85) in classifying hot flashes, with poorest performance among women with high body mass index or anxiety. SVM models showed improved performance (sensitivity=0.89, specificity=0.96, PPV=0.85, NPV=0.96). SVM may improve the performance of skin conductance measures of hot flashes.