COMPARISON OF MACHINE LEARNING MODELS FOR AUTOMATED AUTISM DIAGNOSIS
نویسندگان
چکیده
Autism Spectrum Disorder (ASD) is characterized by impairment in communication and language skills as well repetitive stereotyped behaviors. Early ASD diagnosis helps developing a meaningful outcome its treatment. Machine learning (ML) models can provide faster diagnostic capacity to determine patterns not observable humans through behavioral analysis. We applied the ML classification models, including random forest, logistic regression, K-nearest neighbor intuition, support vector machine, decision tree, kernel Naive Bayes, for each data set (children, teenagers, adults). Our results show that are powerful tools assist healthcare professionals diagnosing ASD. model predicts non-autism cases with 97.9% accuracy. believe performing regression analysis indicating which factors increase or decrease probability of significant contribution. hope elucidate alternative ways objectively diagnose timely treatment purposes.
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ژورنال
عنوان ژورنال: Foco
سال: 2023
ISSN: ['1981-223X']
DOI: https://doi.org/10.54751/revistafoco.v16n6-104