Ensemble Learning Model for Screening Autism in Children
| Autor: | Najah Al-Shanableh, Mofleh Al Diabat |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Autistic spectrum disorder Process (engineering) 05 social sciences 02 engineering and technology medicine.disease Machine learning computer.software_genre Ensemble learning Test (assessment) ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering medicine ComputingMilieux_COMPUTERSANDSOCIETY Autism Learning methods 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer Reliability (statistics) 050104 developmental & child psychology |
| Zdroj: | International Journal of Computer Science and Information Technology. 11:45-62 |
| ISSN: | 0975-3826 0975-4660 |
| Popis: | Autistic Spectrum Disorder (ASD) is a neurological condition associated with communication, repetitive, and social challenges. ASD screening is the process of detecting potential autistic traits in individuals using tests conducted by a medical professional, a caregiver, or a parent. These tests often contain large numbers of items to be covered by the user and they generate a score based on scoring functions designed by psychologists and behavioural scientists. Potential technologies that may improve the reliability and accuracy of ASD tests are Artificial Intelligence and Machine Learning. This paper presents a new framework for ASD screening based on Ensembles Learning called Ensemble Classification for Autism Screening (ECAS). ECAS employs a powerful learning method that considers constructing multiple classifiers from historical cases and controls and then utilizes these classifiers to predict autistic traits in test instances. ECAS performance has been measured on a real dataset related to cases and controls of children and using different Machine Learning techniques. The results revealed that ECAS was able to generate better classifiers from the children dataset than the other Machine Learning methods considered in regard to levels of sensitivity, specificity, and accuracy. |
| Databáze: | OpenAIRE |
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