Abstract
Autism spectrum disorder (ASD) is a serious agitation of the present day. It is a major neurodevelopmental disorder that impairs the children’s ability with autism to recognize the object, face, express emotions, and social interaction. It is not curable but the early-stage detection of ASD can assist in better treatment for them. In this paper, we considered children and adult datasets for further process. The different feature selection algorithms such as Learning Vector Quantization (LVQ), Correlation-based feature selection (CFS), Chi-Squared (CHI) attribute evaluation, Information Gain (IG) attribute evaluation, Gain Ratio (GR) attribute evaluation, and Relief-F (RF) attribute evaluation were applied to reduce features of these datasets. The three different classifiers such as Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were considered for the reduced datasets, and verified their performance using evaluation metrics including predictive accuracy, kappa statistics, precision, recall, and error rate. We considered the k-fold cross-validation with k values 5, 10, and 15. We analyzed the results, and the performance of SVM is best in different evaluation metrics for both datasets. The outcomes of these analytical viewpoints suggest that this proposed machine learning framework can perform better predictions of ASD or not.
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Bala, M., Prova, A.A., Ali, M.H. (2022). Prediction of Autism Spectrum Disorder Using Feature Selection and Machine Learning Algorithms. In: Bansal, R.C., Zemmari, A., Sharma, K.G., Gajrani, J. (eds) Proceedings of International Conference on Computational Intelligence and Emerging Power System. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-4103-9_12
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