The work utilizes the genome-wide association data sourced from subjects diagnosed with autism spectrum disorder (ASD) - Stage I and II. This system facilitates early and definitive prediction of autism to start intervention techniques at a young age. The Decision Tree model is used to classify the incoming gene-expression. Through the model selection stage, the performance of Decision-Trees and Random Forests was evaluated. This method of diagnosis, a combination of Machine Learning and Precision Medicine, ensures direct and a more conclusive diagnosis that can be adapted at feasible costs. The results are presented on a webpage using the Flask web framework.
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Research Article| May 26 2022
Genome-wide autism prediction
J. Anith Ruth;
AIP Conf. Proc. 2464, 060008 (2022)
R. Uma, P. Ramkumar, J. Anith Ruth, R. Valarmathi; Genome-wide autism prediction. AIP Conf. Proc. 26 May 2022; 2464 (1): 060008. https://doi.org/10.1063/5.0082746
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