The early diagnosis in Autism Spectrum Disorder (ASD) is crucial for timely interventions to address the patients’ attentional and social challenges. The currently study aims to use machine learning algorithms to accurately predict ASD outcomes. Dataset from a Kaggle competition was used to perform the prediction analysis. Five supervised machine learning algorithms were employed: Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine Classifier (SVC), Random Forest (RF), and Decision Trees (DT). The models were fine-tuned using a range of possible hyperparameters and evaluated using ROC AUC scores. The best-performing model, Random Forest, achieved a training ROC AUC of 0.93. The model's performance in predicting the unseen test set resulted in a ROC AUC score of 0.8623. The outcome demonstrates the potentials of machine learning models in early prediction of ASD symptoms, which provides support for autistic individuals to enhance their quality of life and education.
Research Article
Open Access