According to the World Health Organization forecast, over 55 million people worldwide have
dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for
patients to plan for the future and deal with the disease. Machine Learning algorithms allow us
to solve the problems associated with early disease detection. This work attempts to identify the
current relevance of the application of machine learning in dementia prediction in the scientific
world and suggests open fields for future research. The literature review was conducted by
combining bibliometric and content analysis of articles originating in a period of 20 years in
the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly,
of which a limited number focused on machine learning in dementia diagnosis. After the exclusion
process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the
past five years in the theme of machine learning in dementia shows that it is a relevant field for
research with still open questions. The methods used to identify dementia or what features are
used to identify or predict this disease are explored in this study. The literature review revealed
that most studies used magnetic resonance imaging (MRI) and its types as the main feature,
accompanied by demographic data such as age, gender, and the mini-mental state examination
score (MMSE). Data are usually acquired from the Alzheimer’s Disease Neuroimaging Initiative
(ADNI). Classification of Alzheimer’s disease is more prevalent than prediction of Mild Cognitive
Impairment (MCI) or their combination. The authors preferred machine learning algorithms
such as SVM, Ensemble methods, and CNN because of their excellent performance and results
in previous studies. However, most use not one machine-learning technique but a combination
of techniques. Despite achieving good results in the studies considered, there are new concepts
for future investigation declared by the authors and suggestions for improvements by employing
promising methods with potentially significant results.
