Enhanced Prediction of Early-Stage Autism Using Supervised Learning with Optimized Feature Engineering

An autism spectrum disease (ASD) affects a person's brain development. Autism spectrum disorder is detected in children and young adults. For an early diagnosis of the disease, parental observation is crucial. That is why it is possible to provide children with effective therapy or treatment when they are young. Misdiagnosis is common due to the wide range of symptoms and levels of autism range syndrome. The datasets on children and toddlers with ASD that were identified at an early age were subjected to a limited number of Feature Transformation methods. The next step was to determine which classification methods performed best by applying them to various ASD datasets. Log, Power-Box Cox, and Yeo-Johnson Transformations for toddler datasets and Log transformations for children’s datasets were the FTs that followed the best prediction. The next step in using these altered datasets for early autism prediction is to use characteristic Selection Techniques in order to identify the most important ASD risk characteristic. In this comparison, the proposed method is the top machine learning technique with an incredible accuracy. Decision Tree, Random Forest (RF), K-Nearest Neighbours(KNN), Support Vector Machine(SVM), and Gradient Descent(GD) are some of the other models that perform a decent job, but they can't compare to the outstanding results that proposed method obtained. With an accuracy rate of 98.99%, proposed method stands out as the most reliable and efficient alternative among these methods.