Hair Loss Diagnosis Using Artificial Neural Networks
Hair is an appendage of the skin that plays an important role in the beauty of people\'s face. Daily averages of 50 to 80 hairs are shed naturally. Various factors are effective in hair loss. In this paper using the eight influence attributes of gender, age, genetic factors, surgery, pregnancy, Zinc deficiency, iron deficiency, anemia and the use of cosmetics, the amount of hair loss is predicted. This work has been performed using artificial neural networks. 60 percent of the collected data was used for train, 20 percent for validation and the remaining 20 percent is used for testing the neural networks. For this, various training algorithms has been used. The result of the implementation of these algorithms has been compared. It seems that neural networks can be successful to predict hair loss.
Keywords: Hair Loss, Artificial Neural Network, Genetic Factor, Iron deficiency
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ABOUT THE AUTHORS
Ahmad Esfandiari
Ahmad Esfandiari received the B.S. in computer engineering from Faculty Engineering Sciences, Islamic Azad University Sari Branch, IRAN in 2003. He received the M.Sc. in computer engineering (Artificial Intelligence) from engineering sciences department, Islamic Azad University Science and Research Branch, IRAN in 2008. He now preparing for Ph.D. degree in Artificial Intelligence. He is a faculty member of Islamic Azad University Sari Branch. His interests include signal processing, image processing, pattern recognition.
Kimia Rezaei Kalantari
Kimia Rezaei Kalantari received the B.S. in computer engineering from Iran university of Science and Technology Behshahr Branch in 2005. He received the M.Sc. in computer engineering from engineering sciences department, Islamic Azad University Qazvin Branch, IRAN in 2009. He now is a faculty member of Islamic Azad University Sari Branch.
Abdorreza Babaei
Abdorreza babaei received the B.S. in computer engineering from Faculty Engineering Sciences, Islamic Azad University Sari Branch, IRAN in 2005. He received the M.Sc. in computer engineering (software engineering) from engineering sciences department, Islamic Azad University Science and Research Branch, IRAN in 2011. He now is a faculty member of Islamic Azad University Sama Organization Sari Branch.
Ahmad Esfandiari
Ahmad Esfandiari received the B.S. in computer engineering from Faculty Engineering Sciences, Islamic Azad University Sari Branch, IRAN in 2003. He received the M.Sc. in computer engineering (Artificial Intelligence) from engineering sciences department, Islamic Azad University Science and Research Branch, IRAN in 2008. He now preparing for Ph.D. degree in Artificial Intelligence. He is a faculty member of Islamic Azad University Sari Branch. His interests include signal processing, image processing, pattern recognition.
Kimia Rezaei Kalantari
Kimia Rezaei Kalantari received the B.S. in computer engineering from Iran university of Science and Technology Behshahr Branch in 2005. He received the M.Sc. in computer engineering from engineering sciences department, Islamic Azad University Qazvin Branch, IRAN in 2009. He now is a faculty member of Islamic Azad University Sari Branch.
Abdorreza Babaei
Abdorreza babaei received the B.S. in computer engineering from Faculty Engineering Sciences, Islamic Azad University Sari Branch, IRAN in 2005. He received the M.Sc. in computer engineering (software engineering) from engineering sciences department, Islamic Azad University Science and Research Branch, IRAN in 2011. He now is a faculty member of Islamic Azad University Sama Organization Sari Branch.
