Autism Spectrum Disorder Analysis Using Logistic Regression, XGB Classifier and SVM.

The complex neurodevelopmental illness known as autism spectrum disorder (ASD) is typified by difficulties with speech and social interaction, as well as repetitive behaviors or limited interests. The word “spectrum” is used to describe the condition because of the vast range of intensity and expression of these symptoms. Research indicates that a mix of environmental and genetic factors may have a role in the development of ASD, even though the precise etiology is still unknown. To support people with ASD, early identification and intervention are essential since they can enhance independence and long-term results. Behavioral therapy, speech therapy, and individually designed educational activities are common treatment modalities. ASD still poses difficulties in social inclusion, healthcare, and education despite advances in knowledge, underscoring the need for continued study and education to improve the lives of people with ASD and their families. This work highlights the necessity of ongoing developments in data gathering and computational methods to increase the accuracy of autism prediction models, which will ultimately support improved clinical judgment and early intervention tactics. A person with autism who has impaired prediction abilities lives in a world that seems “magical” and where things happen suddenly and without reason. Immersion in such an unpredictable environment can be debilitating and impair one’s capacity to engage with it.