Feature Selection for Application on Predicting Alzheimer’s Disease Progress
Seyed Arsalan Hoseyni1, Javad Zaree2, Pejman Masoud3, Zahedizadeh4
1Seyed Arsalan Hoseyni, Department of Electronic Engineering, Bushehr Branch Islamic Azad University, Bushehr Iran.
2Md Javad Zaree, Department of Electronic Engineering, Bushehr Branch Islamic Azad University, Bushehr Iran.
3Pejman Mohammadi, Department of Electronic Engineering, Bushehr Branch Islamic Azad University, Bushehr Iran.
4Masoud Zahedizadeh, Department of Electronic Engineering, Bushehr Branch Islamic Azad University, Bushehr Iran.
Manuscript received on 13 February 2014 | Revised Manuscript received on 20 February 2014 | Manuscript Published on 28 February 2014 | PP: 23-26 | Volume-3 Issue-9, February 2014 | Retrieval Number: I1463023914/14©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, the Bayes classifier is used to predict Alzheimer’s disease progress. The classifier is trained on a subset of the Alzheimer’s Disease Neuroimaging Initiative database. Subjects are diagnosed by doctors as belonging to healthy, mild cognitive impaired, and Alzheimer’s disease class. A software tool for features selection and time regression is developed. The tool utilizes a variant of the Sequential Forward Selection (SFS) algorithm for feature selection, where the criterion used for selecting features is the correct classification rate of the Bayes classifier. The tool also employs linear regression to predict future values of selected biomarkers from past measurements, so that future class of the subject can be predicted.
Keywords: Feature Selection, Alzheimer, Prediction.
Scope of the Article: Application Specific ICs (ASICs)