Comparison of Machine Learning Algorithms for Autism Spectrum Disorder Classification
Erina S. Dewi, Elly M. Imah
Elly M. Imah
Available Online 24 November 2020.
- https://doi.org/10.2991/aer.k.201124.028How to use a DOI?
- Autism Spectrum Disorder, KNN, SVM, Random Forest, Deep Learning
- Autism Spectrum Disorder is one of the fastest-growing neurodevelopmental disorders in the world. These neurodevelopmental disorders often attack children, affecting social development and behavior. Effective early detection of ASD is needed to reduce the risk of ASD in children. This study classified the ASD using KNN, SVM, Random Forest, Backpropagation, and Deep Learning. The Children ASD dataset consists of 292 subjects, 49 ASD subjects and 243 normal subjects. This adolescents ASD dataset consisting of 104 subjects with 63 ASD subjects and 41 normal subjects. The classification process is done by applying some parameters to each algorithm. The algorithms performance in classifying ASD dataset was compared. Based on the specificity and sensitivity value of the Random Forest algorithm with full features is the best algorithm compared to other algorithms in classifying ASD in children and adolescents.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Erina S. Dewi AU - Elly M. Imah PY - 2020 DA - 2020/11/24 TI - Comparison of Machine Learning Algorithms for Autism Spectrum Disorder Classification BT - Proceedings of the International Joint Conference on Science and Engineering (IJCSE 2020) PB - Atlantis Press SP - 152 EP - 159 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.201124.028 DO - https://doi.org/10.2991/aer.k.201124.028 ID - Dewi2020 ER -