Analysis and Classification of Autism Data Using Machine Learning Algorithms
DOI:
https://doi.org/10.25271/sjuoz.2022.10.4.1036Keywords:
Autism, Supervised Learning, Classification, Artificial Neural Networks, PCA, SMOTEAbstract
Autism is a neurodevelopmental disorder that affects children worldwide between the ages of 2 and 8 years. Children with autism have communication and social difficulties, and the current standardized clinical diagnosis of autism still relies on behaviour-based tests. The rapidly growing number of autistic patients in the Kurdistan Region of Iraq necessitates. However, such data are scarce, making extensive evaluations of autism screening procedures more difficult. For this purpose, the use of machine learning algorithms for this disease to assist health practitioners if formal clinical diagnosis should be pursued was investigated. Data from 515 patients were collected in Dohuk city related to autism screening for young children. Three classification algorithms, namely (DT, KNN, and ANN) were applied to diagnose and predict autism using various rating scales. Before applying the above classifiers, the newly obtained data set was in different ways undergo data reprocessing. Since our data is unbalanced with high dimensionality, we suggest combining SMOTE (Synthetic Minority Hyper sampling Technique) and PCA (Primary Component Analysis) to improve the performance of classification models. Experimental results showed that the combination of PCA and SMOTE methods improved classification performance. Moreover, ANN exceeded the other models in terms of accuracy and F1 score, suggesting that these classification methods could be used to diagnose autism in the future.
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