Prognosis of Alzheimer’s disease (AD) progression has been recognized as a challenging problem due to the massive numbers of cognitive, and pathological features recorded for patients and controls. While there have been many studies investigated the diagnosis of dementia using pathological characteristics, predicting the advancement of the disease using cognitive elements has not been heavily studied particularly using technologies like artificial intelligence and machine learning. This research aims at evaluating items of the Alzheimer’s Disease Assessment Scale-Cognitive 13 (ADAS-Cog-13) test to determine key cognitive items that influence the progression of AD. A methodology that consists of machine learning and feature selection (FS) techniques was designed, implemented, and then tested against real data observations (cases and controls) of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository with a narrow scope on cognitive items of the ADAS-Cog-13 test. Results obtained by ten-fold cross validation and using dissimilar classification and FS techniques revealed that the decision tree algorithm produced classification models with the best performing results from the cognitive items. For ADAS-Cog-13 test, memory and learning features including word recall, delayed word recall and word recognition were the key items pinpointing to AD advancement. When these three cognitive items are processed excluding demographics by C4.5 algorithm the models derived showed 82.90% accuracy, 87.60% sensitivity and 78.20% specificity.