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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