MACHINE LEARNING MODEL FOR AUTISM PREDICTION IN TODDLERS
Main Article Content
Abstract
Autism Spectrum Disorder (ASD), commonly referred to as "autism," is a psychiatric condition that affects a person's linguistic, cognitive, and social abilities. It's a prevalent disorder, with approximately 1 in every 54 births being diagnosed with ASD, and about 1% of the global population living with it. Unfortunately, despite its prevalence, the cause and cure for autism remain unknown, posing significant challenges to parents who suspect their child might have ASD. Early diagnosis of autism is crucial for a child's development, but it can be incredibly tough since symptoms manifest as the child grows. Typically, diagnostic tests conducted on children between the ages of 2 to 3 years are less reliable than those performed on children aged 4 to 5 years. This creates a worrying situation because early diagnosis is vital for autistic individuals to reach their developmental milestones successfully. Autism is often characterized by difficulties in social interaction and communication, making it challenging to diagnose accurately even with advanced tools like the ADOS and ADI. This work addresses the concerns surrounding autism diagnosis by focusing on improving the diagnostic pipeline. It involves training and testing machine learning models i.e., Random Forest with Standard scaler using an autism spectrum disorder dataset to identify the most significant indicators of autism in toddlers. The goal is to develop a quantitative approach to aid in early screening and subsequent treatment, as timely intervention can help mitigate long-term symptoms associated with autism. By leveraging machine learning, this work aims to provide valuable insights into diagnosing autism effectively and facilitating better support for individuals with ASD and their families.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.