Alzheimer Hastalığının Tespitinde Makine Öğrenmesi Algoritmalarının Karşılaştırılması

Abstract

Alzheimer's disease is a neurodegenerative disorder that causes loss of cognitive function and cognitive decline in individuals. Detection of the disease at an early stage is important to slow down the devastating effects of the disease. The use of an autonomous computerized support system that can assist specialist physicians in the diagnostic process saves time and helps reduce human error. For this reason, a high-accuracy classification study was aimed at utilizing different machine learning algorithms for early diagnosis of Alzheimer's disease. Within the scope of this study, an open source data set created with Electroencephalogram (EEG) signals from 24 healthy and 24 Alzheimer's patient volunteers was used. 28 features, including spectral and statistical features, were extracted from each channel of the EEG signals. The extracted features were evaluated to the feature importance algorithm and the five most significant features that could distinguish between Alzheimer's individuals and healthy individuals were determined. Four machine learning algorithms are trained with the determined features. 70% of the data was used for training and the algorithms were trained with a 10-fold cross-validation method. When the four machine learning algorithms were tested with the data reserved for testing, which the algorithms had not seen before, the highest accuracy was obtained with the Gradient Boosting Classifier (GBC) algorithm with 96.43%.