Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot

Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot

Linah Wafai (Victoria University, Australia), Aladin Zayegh (Victoria University, Australia), Rezaul K. Begg (Victoria University, Australia) and John Woulfe (Boronia Podiatry, Australia)
Copyright: © 2015 |Pages: 11
DOI: 10.4018/978-1-4666-5888-2.ch545
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Background

Today’s ever-changing lifestyle has seen the manifestation of a diabetic epidemic become deeply ingrained into our society. The diabetic population has shifted over recent years towards being an alarmingly younger, and more overweight population. The growing incidence of diabetes diagnosis throughout the world has sparked many health concerns, instigating a wave of diabetic research to not only deal with the disease itself, but also the many consequent, and often quite debilitating, complications brought about by diabetes. Foot ulcers, in particular, have proven to be a great cause for concern as one of the most severe complications to affect diabetic individuals on a global scale. In fact foot ulcers can have detrimental effects on a diabetic patient’s wellbeing and quality of life. Moreover, management and treatment of these ulcers places a substantial financial burden on both patients and the healthcare system (Snyder & Hanft, 2009; Winkley et al., 2012; Winkley, Stahl, Chalder, Edmonds, & Ismail, 2009).

Key Terms in this Chapter

Artificial Neural Networks: A method that is capable of learning, storing knowledge, and making decisions based on data it has been exposed to.

Fuzzy Logic: A precise, linguistic-based method for dealing with approximations derived from imprecise data.

Neuropathy: Damage to nerves in the peripheral nervous system which can cause numbness, weakness and partial or complete loss of sensation in the limbs.

Plantar Pressure: Measurement of the perpendicular forces applied to the plantar surface of the foot.

Support Vector Machines: A supervised learning method with enhanced capabilities for dealing with complex, non-linear data to solve classification and regression problems.

Diabetic Foot: Any foot pathology that forms as a direct consequence of diabetes, such as foot ulcers.

Hybrid Techniques: A combination of two or more computational techniques which provide greater advantages than individual techniques and improve data analysis.

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