Publications by authors named "Zhong-Quan Jiang"
Front Psychiatry
September 2022
Article Synopsis
- The study investigates how effective machine learning (ML) methods are in diagnosing autism spectrum disorder (ASD) with comorbid intellectual disability (ID) compared to traditional regression models.
- From 241 children with ASD, various models (Logistic Regression, Support Vector Machine, Random Forest, and XGBoost) were trained using demographic and behavioral data to identify those with ID.
- The results showed that while all models were effective, Support Vector Machine had the highest overall accuracy (83.6%), with Logistic Regression having the best sensitivity, indicating that ML methods are promising for early detection in primary care settings.
Front Psychiatry
August 2022
Article Synopsis
- * Early diagnosis of NDDs remains challenging, leading to delays in intervention and affecting patient outcomes.
- * Machine learning (ML) technologies are being explored for improving early detection and treatment of NDDs in children, offering new insights through data analysis.
