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

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