Open AccessMachine Learning Method to Differentiate Ataxias
Author(s) -
Gustavo Simões Carnivali,
AUTHOR_ID
Publication year - 2021
Publication title -
international journal of applied mathematics and machine learning
Language(s) - English
Resource type - Journals
ISSN - 2394-2258
DOI - 10.18642/ijamml_710012230
Subject(s) - spinocerebellar ataxia , ataxia , computer science , machine learning , decision tree , computational biology , artificial intelligence , medicine , biology , neuroscience
Spinocerebellar ataxias or SCAs, are a group of more than 37 genetically and clinically heterogeneous known neurodegenerative diseases. This work analyzes the level of genetic similarity between several ataxias, we identified proteins that are associated with more than one ataxia. A decision tree was trained to identify ataxias by identifying whether a new entry disease not yet identified and not classified can be grouped as an ataxia. Altogether 12 proteins from different ataxias were verified, all 12 proteins were analyzed in 500 different trees to better evaluate the method used. Of the 12 proteins tested, the method was correct for 10 different proteins or 83% of correct results. This identifier and the results obtained in the experiments allow a greater characterization of the diseases addressed, it also allows applications such as the reuse of treatments for similar diseases.