  Dementia is a neuropsychiatric brain disorder that usually occurs when one or
more brain cells stop working partially or at all. Diagnosis of this disorder
in the early phases of the disease is a vital task to rescue patients lives
from bad consequences and provide them with better healthcare. Machine learning
methods have been proven to be accurate in predicting dementia in the early
phases of the disease. The prediction of dementia depends heavily on the type
of collected data which usually are gathered from Normalized Whole Brain Volume
(nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected
from Magnetic Resonance Imaging (MRIs). Other biological features such as age
and gender can also help in the diagnosis of dementia. Although many studies
use machine learning for predicting dementia, we could not reach a conclusion
on the stability of these methods for which one is more accurate under
different experimental conditions. Therefore, this paper investigates the
conclusion stability regarding the performance of machine learning algorithms
for dementia prediction. To accomplish this, a large number of experiments were
run using 7 machine learning algorithms and two feature reduction algorithms
namely, Information Gain (IG) and Principal Component Analysis (PCA). To
examine the stability of these algorithms, thresholds of feature selection were
changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has
resulted in 7x9 + 7x7= 112 experiments. In each experiment, various
classification evaluation data were recorded. The obtained results show that
among seven algorithms the support vector machine and Naive Bayes are the most
stable algorithms while changing the selection threshold. Also, it was found
that using IG would seem more efficient than using PCA for predicting Dementia.
