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- W4308443892 abstract "Autism spectrum disorder (ASD) is a severe developmental disorder that impacts the capacity of patient to communicate and interact. Its symptoms are observed in the initial developmental stages and persists entire lifetime. Though the reasons of ASD are in genetics or in environmental factors, still their life can be made better by diagnosing and providing treatment to them at early phase. In present time, scientific medical test carried for ASD diagnosis is not only time-consuming but also costly. Machine learning has shown outstanding results in various domains including health care. In order to enhance the correctness (accuracy) and duration needed for early identification, machine learning methods can be incorporated to balance the traditional usual procedures. The proposed model with machine learning classifiers is applied on our dataset, and predictive model is built using generated results. The key aim of this proposed work is to predict whether the patient is at risk of having ASD which would help in the diagnosis process so that suitable treatment and therapy can be recommended to patients and hence they can live normal lifestyle. By observing the results achieved, it is clear that decision tree provides maximum accuracy on autism dataset." @default.
- W4308443892 created "2022-11-11" @default.
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- W4308443892 date "2022-11-08" @default.
- W4308443892 modified "2023-09-30" @default.
- W4308443892 title "Content-Centric Prediction Model for Early Autism Spectrum Disorder (ASD) Screening in Children" @default.
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- W4308443892 doi "https://doi.org/10.1007/978-981-19-5331-6_38" @default.
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