AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
XGBoost is the most suitable algorithm for clinical decision support in early ASD screening within the scope of this dataset, indicating strong generalizability.
The proposed web-based system is a valuable tool for supporting early ASD detection in under-resourced environments, as its combination of validated screening tools and machine learning predictions enhances diagnostic workflows, enabling earlier intervention and better clinical decision-making.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder which necessitates early and precise diagnosis to facilitate prompt intervention and enhance long-term results. Using behavioural and demographic data, this research offers a machine learningbased method for identifying ASD. The proposed system incorporate...
The need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.
The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and...
A comprehensive machine learning framework to classify ASD severity (mild, moderate, severe) is developed and validates by investigating the differential impact of feature engineering and selection, revealing a critical “evaluation paradox” where radical, unguided feature reduction improved geometric cluster cohesion b...
This review evaluates the role of machine learning and deep learning approaches in improving ASD prediction, with a particular focus on two important yet relatively underexplored methodological components: data augmentation and feature selection.
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