On the Utility of Parents' Historical Data to Investigate the Causes of Autism Spectrum Disorder: A Data Mining-Based Framework

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Objective: Autism Spectrum Disorder (ASD) is acknowledged as a challenge that influences the learning ability of adolescents and also negatively impacts their families. Autism may be caused due to environmental exposure or genetically inherited disorder, however, no definitive or universally customary reasons are known. This makes the issue fairly challenging. Material and methods: This work focuses on identifying the reasons of ASD utilizing computational methods. For this, data is collected that focuses on parental history for finding the trigged features by reviewing antenatal, perinatal, and infant hazard factors of ASD. Afterwards, ML techniques are applied on the collected instances to develop a predictive model and identify the reasons to ASD. While collecting the data, samples are obtained for ASD and non-ASD individuals both. A total of 115 features are obtained from each subject. The collected dataset has 47% samples of the subjects with ASD. Dimensionality reduction, and four feature selection methods are applied on the data to eliminate noise and least valued features. The data is verified using two clustering techniques, i.e., k-means and k-medoid. To validate the clustering results five clustering validation indices are used. Later, three classifiers, i.e. k-nearest neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are trained to predict cases with ASD. The frequent items mining technique and the descriptive analysis of the clustered data are utilized to identify the factors that may cause ASD. Results: The proposed framework enables to identify the features that may contribute towards ASD. Whereas, for the classification part, SVM classifier performs better than others do with an average accuracy of 98.34% in predicting the ASD cases. Conclusion: The results identified stress as the dominant feature and environmental factors, like frequent use of canned food and plastic/steel bottles during fertilization period that may contribute towards ASD.




Elsevier BV






Computer Sciences | Medicine and Health Sciences


Autism spectrum disorder, Clustering, Data-driven investigation, Machine learning, Prediction

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Open Access