Classification of Brain Volumetric Data to Determine Alzheimer’s Disease Using Artificial Bee Colony Algorithm as Feature Selector

Abstract

Alzheimer’s disease is a degenerative disease that affects the progression of age and causes the brain to be unable to fulfill its expected functions. Depending on the stage, the effects of Alzheimer’s disease (AD) vary from forgetting the names of surrounding people to not being able to continue daily life without assistance. To the best of our knowledge, there are no generally accepted diagnostic or treatment methods. In this study, a binary version of the artificial bee colony algorithm (BABC) is proposed as a feature selector for classifying AD from volumetric and statistical data of brain magnetic resonance images (MRIs). MRIs were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Volumetric and statistical data from the collected MRIs were obtained from an online system called volBrain. Then, for comparison, binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), and binary differential evolution (BDE) were employed. The results of this comparison show that BGWO outperforms BABC, which is a competitive method for this purpose. Additionally, traditional data mining methods such as Info Gain (IG), Gain Ratio (GR), Chi-square (CHI), and ReliefF methods were utilized for comparison. The results also demonstrate the superiority of the BABC over traditional methods. Second, this study focused on exploring which parts of the brain are more relevant for AD diagnosis. The novelty of this study lies in the output of the second point.