Autism screening: an unsupervised machine learning approach.

Early screening of autism spectrum disorders (ASD) is a key area of research in healthcare. Currently artificial intelligence (AI)-driven approaches are used to improve the process of autism diagnosis using computer-aided diagnosis (CAD) systems. One of the issues related to autism diagnosis and screening data is the reliance of the predictions primarily on scores provided by medical screening methods which can be biased depending on how the scores are calculated. We attempt to reduce this bias by assessing the performance of the predictions related to the screening process using a new model that consists of a Self-Organizing Map (SOM) with classification algorithms. The SOM is employed prior to the diagnostic process to derive a new class label using clusters learnt from the independent features; these clusters are related to communication, repetitive traits, and social traits in the input dataset. Then, the new clusters are compared with existing class labels in the dataset to refine and eliminate any inconsistencies. Lastly, the refined dataset is utilised to derive classification systems for autism diagnosis. The new model was evaluated against a real-life autism screening dataset that consists of over 2000 instances of cases and controls. The results based on the refined dataset show that the proposed method achieves significantly higher accuracy, precision, and recall for the classification models derived when compared to models derived from the original dataset.