Title:Machine Learning and Social Network Analysis Applied to Alzheimer's Disease Biomarkers
VOLUME: 13 ISSUE: 5
Author(s):Javier Di Deco, Ana M. Gonzalez, Julia Diaz, Virginia Mato, Daniel Garcia–Frank, Juan Alvarez–Linera, Ana Frank and Juan A. Hernandez–Tamames
Affiliation:Instituto de Ingenieria del Conocimiento, Cantoblanco, Madrid, Spain.
Keywords:Alzheimer's Disease, Feature selection, Machine learning, Magnetic resonance imaging, Social network analysis.
Abstract:Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in
early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological,
biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount
of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of
the normal ageing.
Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers
are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new
patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and
connections. The resulting graph-based structures are often very complex; there can be many kinds of connections
between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in
medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study.
This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic
resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different
phenotypes in dementia, in particular, different stages of Alzheimer's disease.
