Abstract
The study of microbiome composition is showing positive indication for the utilization in the diagnosis and treatment of many conditions and diseases. One such condition is an autism spectrum disorder (ASD). In this paper, we analyzed a dataset of 58 children’s gut microbiome 16 s rRNA sequenced samples from Ecuador. The 31 samples in the dataset were from the neurotypical children (Control group), while 27 were from the children with ADS symptoms. We analyzed the dataset and reported the most statistical significance between the two studied groups. Furthermore, we applied the Random Forest (RF) machine learning algorithm to develop a classifier that distinguishes neurotypical samples from the ASD samples. The features of the classifier were relative abundances of genus-level bacteria in each sample. The best performance of the classifier (with 5-fold cross-validation) was exhibited when the top five features were used (as identified by RF feature importance metric). The overall accuracy was (83.3%). The ASD samples were identified with 75% accuracy while neurotypical samples were identified with 87.5% accuracy.
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Telalovic, J.H., Pasic, L., Cicak, D.B. (2021). The Use of Data Science for Decision Making in Medicine: The Microbial Community of the Gut and Autism Spectrum Disorders. In: Hasic Telalovic, J., Kantardzic, M. (eds) Mediterranean Forum – Data Science Conference. MeFDATA 2020. Communications in Computer and Information Science, vol 1343. Springer, Cham. https://doi.org/10.1007/978-3-030-72805-2_6
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