Analysis of Palm Perspiration Effect with SVM for Diabetes in People
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.
[1] Arısoy, E.S., T.C. Ministry of Health, Diagnosis and Treatment Guides
for Primary Healthcare 2003,T.C. Miistry of Health, 2003
[2] Wild S., Roglic G, Green A, Sicree R, King H. "Global prevalence of
diabetes estimates for the year 2000 and projections for 2030", Diabetes
Care, 2004, Vol 27, pp 1047-1053
[3] International Diabetes Federation. Diabetes Atlas, World Diabetes
Foundation, 2006
[4] Satman I., "The update criteria and the reasons of them in diagnosis and
follow up of diabetes mellitus",A T├╝rkiye Klinikleri Journal of Internal
Medical Sciences, 2007,Vol 3,pp 1-15
[5] Temurtas, H., Yumusak, N., & Temurtas, F. (2009), "A comparative
study on diabetes disease diagnosis using neural networks", Expert
Systems with Applications, 36, 8610-8615.
[6] Mohamed, E. I., Linderm, R., Perriello, G., Di Daniele, N., Poppl, S. J.,
& De Lorenzo, A. (2002), "Predicting type 2 diabetes using an
electronic nose-base artificial neural network analysis", Diabetes
Nutrition & Metabolism, 15(4), 215-221.
[7] Saraoglu, H.M., & Kocan, M. (2010), "A study on non-invasive
detection of blood glucose concentration from human palm perspiration
by using artificial neural networks", Expert Systems, 27(3), 156-165.
[8] Diabetes Care, American Diabetes Association, January 2008, Vol
31,pp 55-60
[9] Diabetes Care, American Diabetes Association, January 2010, Vol
33,pp 11-13
[10] Diabetes Voice, December 2007, Vol 52, pp 33-34
[11] Kamei, T., T. Tsuda, Y. Mibu, S. Kitagawa, H. Wada, K. Naitoh and K.
Nakashima (1998a), "Novel instrumentation for determination of
ethanol concentrations in human perspiration by gas chromatography
and a good interrelationship between ethanol concentrations in sweat
and blood", Analytica Chimica Acta, 365 (1-3), 259-266.
[12] Kamei, T., T. Tsuda, S. Kitagawa, K. Naitoh, K. Nakashima and T.
Ohhashi (1998b), "Physical stimuli and emotional stress-induced sweat
secretions in the human palm and forehead", Analytica Chimica Acta,
365, 319-326
[13] Saraoglu, H. M. ve Koçan M., "Determination of Blood Glucose Level
Based Breath Analysis by a Quartz Crystal Microbalance Sensor Array",
IEEE Sensors Journal, Special Issue on the Future of Sensors and
Instrumentation for Human Breath Analysis, 10, 104-109, 2010
[14] Masumura, M., S. Shinomiya, K. Nagata, T. Kato, Y. Matsuzaki, S.
Nagaoka, R. Inoue, J. Perspiration Res., 3, 43-45, 1996
[15] Kuno, Y., Human Perspiration, C.C. Thomas Publisher, Springfield,IL,
1956.
[16] Ohhashi T., M. Uono (eds.), "Palmer perspiration - measurement and
clinical applications (in Japanese)", Life Medicom Co, Ltd., Nagoya,
1993.
[17] Groscurth, P., "Anatomy of sweat glands". In: Kreyden OP, Bo¨ni R,
Burg G, editors. Hyperhidrosis and botulinum toxin in dermatology.
Basel: Karger, 1-9, 2002.
[18] Kamei, T., T. Tsuda, S. Kitagawa, K. Naitoh, K. Nakashima, T.
Ohhashi, "Physical stimuli and emotional stress-induced sweat
secretions in the human palm and forehead", Analytica Chimica Acta,
365, 319-326, 1998 b
[19] Huestis, M.A., J.M. Oyler , E.J. Cone , A.T. Wstadik , D. Schoendorfer ,
R.E. Joseph JR., "Sweat testing for cocaine, codeine and metabolites by
gas chromatography-mass spectrometry, Journal of Chromatography ",
733, 247-264, 1999.
[20] Chang, B.W., S.J. Yeh, P.P. Tsai, H.C. Chang, "Monitoring perspiration
from palms of hypohidrosis patients with a stopped-flow conductometric
mini-system",Clinica Chimica Acta, 348, 107- 111, 2004.
[21] Asahina, M., A. Suzuki, M. Mori, T. Kanesaka, T. Hattori, "Emotional
sweating response in a patient with bilateral amygdala damage",
International Journal of Psychophysiology, 47, 87-93, 2003.
[22] Saraoglu, H. M. ve Koçan M., "A Study on Non-Invasive Detection of
Blood Glucose Concentration from Human Palm Perspiration by Using
Artificial Neural Networks", Expert Systems, 27(3), 156-165, 2010
[23] Vapnik, V. , Statistical Learning Theory, Wiley,1998
[24] Brownz M.P.S., Grundyz W.N., Linz D., Cristianini N., Sugnet C., Ares,
JR. M., Hausslerz D., "Support Vector Machine Classification of
Microarray Gene Expression Data", UCSC-CRL-99-09
[25] Burges, C. J. C. (1998). "A tutorial on support vector machines for
pattern recognition", Data Mining and Knowledge Discovery, 2(2):121-
167.
[26] V.N. Vapnik, "An Overview of Statistical Learning Theory", IEEE
Trans. Neural Networks 10 (5) (1999).
[27] Saraoglu, H.M., Yildirim, M., Ozbeyaz, A., TEMURTAS, F., (2011),
"Kandaki Glikoz ve HbA1c Diyabet Tanı Değerlerinin Avuç İçi Terleme
Ölçümleri Kullanılarak DVM ile Sınıflandırması", In Proceedings of Tip
Teknolojileri Ulusal Kongresi (Tip Tekno 11), October 13-16, Antalya
[28] (2012),Univesty of Strathclyde website. (Online).
Available:http://www.strath.ac.uk/media/departments/eee/cesip/cesipse
minar/Jinchang_Ren_seminar. Pdf
[29] (2012), A-SVM based server for rice website. (Online).
Available:www.imtech.res.in/raghava/rbpred/algorithm.html.
[1] Arısoy, E.S., T.C. Ministry of Health, Diagnosis and Treatment Guides
for Primary Healthcare 2003,T.C. Miistry of Health, 2003
[2] Wild S., Roglic G, Green A, Sicree R, King H. "Global prevalence of
diabetes estimates for the year 2000 and projections for 2030", Diabetes
Care, 2004, Vol 27, pp 1047-1053
[3] International Diabetes Federation. Diabetes Atlas, World Diabetes
Foundation, 2006
[4] Satman I., "The update criteria and the reasons of them in diagnosis and
follow up of diabetes mellitus",A T├╝rkiye Klinikleri Journal of Internal
Medical Sciences, 2007,Vol 3,pp 1-15
[5] Temurtas, H., Yumusak, N., & Temurtas, F. (2009), "A comparative
study on diabetes disease diagnosis using neural networks", Expert
Systems with Applications, 36, 8610-8615.
[6] Mohamed, E. I., Linderm, R., Perriello, G., Di Daniele, N., Poppl, S. J.,
& De Lorenzo, A. (2002), "Predicting type 2 diabetes using an
electronic nose-base artificial neural network analysis", Diabetes
Nutrition & Metabolism, 15(4), 215-221.
[7] Saraoglu, H.M., & Kocan, M. (2010), "A study on non-invasive
detection of blood glucose concentration from human palm perspiration
by using artificial neural networks", Expert Systems, 27(3), 156-165.
[8] Diabetes Care, American Diabetes Association, January 2008, Vol
31,pp 55-60
[9] Diabetes Care, American Diabetes Association, January 2010, Vol
33,pp 11-13
[10] Diabetes Voice, December 2007, Vol 52, pp 33-34
[11] Kamei, T., T. Tsuda, Y. Mibu, S. Kitagawa, H. Wada, K. Naitoh and K.
Nakashima (1998a), "Novel instrumentation for determination of
ethanol concentrations in human perspiration by gas chromatography
and a good interrelationship between ethanol concentrations in sweat
and blood", Analytica Chimica Acta, 365 (1-3), 259-266.
[12] Kamei, T., T. Tsuda, S. Kitagawa, K. Naitoh, K. Nakashima and T.
Ohhashi (1998b), "Physical stimuli and emotional stress-induced sweat
secretions in the human palm and forehead", Analytica Chimica Acta,
365, 319-326
[13] Saraoglu, H. M. ve Koçan M., "Determination of Blood Glucose Level
Based Breath Analysis by a Quartz Crystal Microbalance Sensor Array",
IEEE Sensors Journal, Special Issue on the Future of Sensors and
Instrumentation for Human Breath Analysis, 10, 104-109, 2010
[14] Masumura, M., S. Shinomiya, K. Nagata, T. Kato, Y. Matsuzaki, S.
Nagaoka, R. Inoue, J. Perspiration Res., 3, 43-45, 1996
[15] Kuno, Y., Human Perspiration, C.C. Thomas Publisher, Springfield,IL,
1956.
[16] Ohhashi T., M. Uono (eds.), "Palmer perspiration - measurement and
clinical applications (in Japanese)", Life Medicom Co, Ltd., Nagoya,
1993.
[17] Groscurth, P., "Anatomy of sweat glands". In: Kreyden OP, Bo¨ni R,
Burg G, editors. Hyperhidrosis and botulinum toxin in dermatology.
Basel: Karger, 1-9, 2002.
[18] Kamei, T., T. Tsuda, S. Kitagawa, K. Naitoh, K. Nakashima, T.
Ohhashi, "Physical stimuli and emotional stress-induced sweat
secretions in the human palm and forehead", Analytica Chimica Acta,
365, 319-326, 1998 b
[19] Huestis, M.A., J.M. Oyler , E.J. Cone , A.T. Wstadik , D. Schoendorfer ,
R.E. Joseph JR., "Sweat testing for cocaine, codeine and metabolites by
gas chromatography-mass spectrometry, Journal of Chromatography ",
733, 247-264, 1999.
[20] Chang, B.W., S.J. Yeh, P.P. Tsai, H.C. Chang, "Monitoring perspiration
from palms of hypohidrosis patients with a stopped-flow conductometric
mini-system",Clinica Chimica Acta, 348, 107- 111, 2004.
[21] Asahina, M., A. Suzuki, M. Mori, T. Kanesaka, T. Hattori, "Emotional
sweating response in a patient with bilateral amygdala damage",
International Journal of Psychophysiology, 47, 87-93, 2003.
[22] Saraoglu, H. M. ve Koçan M., "A Study on Non-Invasive Detection of
Blood Glucose Concentration from Human Palm Perspiration by Using
Artificial Neural Networks", Expert Systems, 27(3), 156-165, 2010
[23] Vapnik, V. , Statistical Learning Theory, Wiley,1998
[24] Brownz M.P.S., Grundyz W.N., Linz D., Cristianini N., Sugnet C., Ares,
JR. M., Hausslerz D., "Support Vector Machine Classification of
Microarray Gene Expression Data", UCSC-CRL-99-09
[25] Burges, C. J. C. (1998). "A tutorial on support vector machines for
pattern recognition", Data Mining and Knowledge Discovery, 2(2):121-
167.
[26] V.N. Vapnik, "An Overview of Statistical Learning Theory", IEEE
Trans. Neural Networks 10 (5) (1999).
[27] Saraoglu, H.M., Yildirim, M., Ozbeyaz, A., TEMURTAS, F., (2011),
"Kandaki Glikoz ve HbA1c Diyabet Tanı Değerlerinin Avuç İçi Terleme
Ölçümleri Kullanılarak DVM ile Sınıflandırması", In Proceedings of Tip
Teknolojileri Ulusal Kongresi (Tip Tekno 11), October 13-16, Antalya
[28] (2012),Univesty of Strathclyde website. (Online).
Available:http://www.strath.ac.uk/media/departments/eee/cesip/cesipse
minar/Jinchang_Ren_seminar. Pdf
[29] (2012), A-SVM based server for rice website. (Online).
Available:www.imtech.res.in/raghava/rbpred/algorithm.html.
@article{"International Journal of Medical, Medicine and Health Sciences:49529", author = "Hamdi Melih Saraoğlu and Muhlis Yıldırım and Abdurrahman Özbeyaz and Feyzullah Temurtas", title = "Analysis of Palm Perspiration Effect with SVM for Diabetes in People", abstract = "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.
", keywords = "Palm perspiration, Diabetes, Support Vector
Machine, Classification.", volume = "6", number = "10", pages = "459-5", }
