Alzheımer hastalığının gen seçimi için mikrodizi veri setleri ve mimari kullanılarak geliştirilmiş biyo-ilhamlı mantaray

Alzheimer's disease (AD), a prominent neurodegenerative condition, poses significant challenges in its early diagnosis and treatment due to its intricate pathophysiology characterized by the presence of amyloid plaques and neurofibrillary tangles. The recent developments in microarray technology, machine learning, artificial intelligence, and data mining analysis techniques hold considerable potential for comprehending the genetic foundations of AD. Nevertheless, the prediction of AD using machine learning faces several challenges surrounding the analysis of genes expression datasets. Summarized in the "curse of dimensionality" caused by the high dimensional disease's microarray datasets, the accurate prediction of disease can significantly suffer from overfitting, bias, and computational demands. To address this challenge, genes selection methods are applied to alleviate the effect of Alzheimer high dimensional genes expression datasets and elevate the overall machine performance.