Foot Classification and Influence of Pattern Recognition
Saleh S. AlTayyar1, Ahmed E. Negm2

1Saleh S. AlTayyar, Biomedical Technology Department, College of applied medical science/ King Saud University, Riyadh, KSA.
2Ahmed E. Negm, Consultant, Barq Consulting Engineer, Healthcare Technology Administration, King Fahad Medical City. Riyadh, KSA.

Manuscript received on 13 April 2017 | Revised Manuscript received on 20 April 2017 | Manuscript Published on 30 April 2017 | PP: 218-225 | Volume-6 Issue-4, April 2017 | Retrieval Number: D4971046417/17©BEIESP
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Abstract: The article presents the application of neural network and decision tree techniques to investigating barometric data got with instruments measuring the weight of the human plantar onto contact surface while strolling. The examination was completed on a gathering of plantar foot photo taken while the subject remained on the reflected photograph box. We gather 35 understanding, 30 of them are male and 5 female with various ages. Numerical qualities for foot examination for every patient foot part get measuring 12 property. Some foot plant pathologies, similar to buckle and level foot, are ordinarily identified by a human master by method for impression pictures. All things considered, the absence of prepared individual to finish such huge first screening discovery endeavors blocks the routinely analytic of the previously mentioned pathologies. In this work an imaginative programmed framework for foot plant pathologies in view of neural systems (NN) and Decision Tree (DT) are introduced. The outcomes accomplished with this framework confirm the attainability of setting up programmed conclusion frameworks in light of the impression and example acknowledgment. The order settled on by the resultant choice tree was right for all the more than 94% steps. This permits to point the parameters which are the best discriminators between the explored sorts of human walk.
Keywords: Foot Deformities, Photography, Pattern Recognition, Neural Network, Decision Trees.

Scope of the Article: Classification