%0 Journal Article
	%A Hamdi Melih Saraoğlu and  Muhlis Yıldırım and  Abdurrahman Özbeyaz and  Feyzullah Temurtas
	%D 2012
	%J International Journal of Biomedical and Biological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 70, 2012
	%T Analysis of Palm Perspiration Effect with SVM for Diabetes in People
	%U https://publications.waset.org/pdf/390
	%V 70
	%X In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.

	%P 489 - 493