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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Amirbay, Aizat
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Computer Science & IT Electrical & Electronics Engineering

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Development of an algorithm for identifying the autism spectrum based on features using deep learning methods Amirbay, Aizat; Mukhanova, Ayagoz; Baigabylov, Nurlan; Kudabekov, Medet; Mukhambetova, Kuralay; Baigusheva, Kanagat; Baibulova, Makbal; Ospanova, Tleugaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5513-5523

Abstract

The presented scientific work describes the results of the development and evaluation of two deep learning algorithms: long short-term memory with a convolutional neural network (LSTM+CNN) and long short-term memory with an autoencoder (LSTM+AE), designed for the diagnosis of autism spectrum disorders. The study focuses on the use of eye tracking technology to collect data on participants' eye movements while interacting with animated objects. These data were saved in NumPy array format (.npy) for ease of later analysis. The algorithms were evaluated in terms of their accuracy, generalization ability, and training time, which was confirmed by experts. The main goal of the study is to improve the diagnosis of autism, making it more accurate and effective. The convolutional neural network long short-term memory and autoencoder-long short-term memory models have shown promise as tools for achieving this goal, with the autoencoder model standing out for its ability to identify internal relationships in data. The article also discusses potential clinical applications of these algorithms and directions for future research.
Autism detection using facial and motor analysis using machine learning Amirbay, Aizat; Baigabylov, Nurlan; Mukhanova, Ayagoz; Mukhambetova, Kuralay; Zaitov, Elyor; Burganova, Roza; Khusanova, Khayriniso; Akhmedova, Feruza
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10319

Abstract

This paper proposes a method for detecting autism spectrum disorders (ASD) through the analysis of facial and motor features using machine learning. The aim is to develop an algorithm for automatic ASD diagnosis based on spatiotemporal behavioral patterns. Traditional diagnostic methods rely on subjective expert observations, often delaying intervention. To address this, a hybrid convolutional neural network and long short-term memory (CNN+LSTM) model was employed. Convolutional layers extracted spatial features from video frames, while recurrent layers tracked temporal dynamics. Using MediaPipe face mesh, pose, and hands models, 1,639 parameters were obtained, including facial and pose coordinates, hand landmarks, mouth aspect ratio (MAR), and motion energy. The dataset comprised 100 children, aged 5–9 years (50 with ASD, 50 typically developing (TD)). Stratified cross-validation was applied to ensure subject-independent evaluation. Results showed 90% accuracy on the training set, 85–90% on validation, and an area under the curve (AUC) greater than 0.90, confirming model stability. Data visualization highlighted significant differences in motor activity and emotional expression between groups. The proposed approach demonstrates the potential for robust and objective ASD detection. It can be applied in clinical and educational contexts to improve early diagnosis and timely intervention.
Co-Authors Akhmedova, Feruza Baibulova, Makbal Baigabylov, Nurlan Baigusheva, Kanagat Burganova, Roza Khusanova, Khayriniso Kudabekov, Medet Mukhambetova, Kuralay Mukhanova, Ayagoz Ospanova, Tleugaisha Zaitov, Elyor