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To assemble the autism prophecy model, the research integrates the Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) machine intelligence methods.This model is engaged to analyse the recommendation dossier and create indicators about either one has ASD.Based on the evaluation's findings, the suggested prediction model performed better than expected for the AQ-10 dataset and the real dataset in terms of performance indicators. This suggests that the model is successful in predicting ASD and has the potential to offer insightful information regarding the diagnosisof autism.Additionally, established the submitted prediction model, a travelling request was founded on account of the research. The model is applied to the smartphone application to provide users with ASD predictions. This makes it convenient and quick to use the prediction model, which may be helpful for people looking for early ASD detection and treatment.
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To assemble the autism prophecy model, the research integrates the Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) machine intelligence methods.This model is engaged to analyse the recommendation dossier and create indicators about either one has ASD.Based on the evaluation's findings, the suggested prediction model performed better than expected for the AQ-10 dataset and the real dataset in terms of performance indicators. This suggests that the model is successful in predicting ASD and has the potential to offer insightful information regarding the diagnosisof autism.Additionally, established the submitted prediction model, a travelling request was founded on account of the research. The model is applied to the smartphone application to provide users with ASD predictions. This makes it convenient and quick to use the prediction model, which may be helpful for people looking for early ASD detection and treatment.