A MACHINE LEARNING CASE STUDY ON EARLY DETECTION OF AUTISM SPECTRUM DISORDER USING PHENOTYPIC DATA
Gawish, M., El-Askary, N., Morsey, M., mahmoud, A., Aref, M., El-Arif, T. (2025). A MACHINE LEARNING CASE STUDY ON EARLY DETECTION OF AUTISM SPECTRUM DISORDER USING PHENOTYPIC DATA. EKB Journal Management System, 25(2), 54-62. doi: 10.21608/ijicis.2025.390933.1399
Mohamed Yahia Gawish; Nada Sherif El-Askary; Mohamed Mabrouk Morsey; Abeer mahmoud mahmoud; Mostafa Aref; Taha Ibrahim El-Arif. "A MACHINE LEARNING CASE STUDY ON EARLY DETECTION OF AUTISM SPECTRUM DISORDER USING PHENOTYPIC DATA". EKB Journal Management System, 25, 2, 2025, 54-62. doi: 10.21608/ijicis.2025.390933.1399
Gawish, M., El-Askary, N., Morsey, M., mahmoud, A., Aref, M., El-Arif, T. (2025). 'A MACHINE LEARNING CASE STUDY ON EARLY DETECTION OF AUTISM SPECTRUM DISORDER USING PHENOTYPIC DATA', EKB Journal Management System, 25(2), pp. 54-62. doi: 10.21608/ijicis.2025.390933.1399
Gawish, M., El-Askary, N., Morsey, M., mahmoud, A., Aref, M., El-Arif, T. A MACHINE LEARNING CASE STUDY ON EARLY DETECTION OF AUTISM SPECTRUM DISORDER USING PHENOTYPIC DATA. EKB Journal Management System, 2025; 25(2): 54-62. doi: 10.21608/ijicis.2025.390933.1399

A MACHINE LEARNING CASE STUDY ON EARLY DETECTION OF AUTISM SPECTRUM DISORDER USING PHENOTYPIC DATA

International Journal of Intelligent Computing and Information Sciences
Volume 25, Issue 2, June 2025, Page 54-62  XML PDF (580.41 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2025.390933.1399
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Authors
Mohamed Yahia Gawish email orcid 1; Nada Sherif El-Askaryorcid 2; Mohamed Mabrouk Morseyorcid 1; Abeer mahmoud mahmoudorcid 3; Mostafa Areforcid 4; Taha Ibrahim El-Ariforcid 5
1Computer Science Department, Faculty of Computer and Information Sciences, Cairo, Egypt
2Computer Science, Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt
3Computer Science department Faculty of Computer and information science, Ain Shams University, Cairo, Egypt
4Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
5Computer Science Dep., Faculty of Computer and Information Sciences, Ain Shams University- Egypt
Abstract
Early Autism Spectrum Disorder (ASD) detection is crucial for promoting cognitive, motor skills, and social development. Artificial intelligence-powered systems present an exciting chance to transform ASD detection. The Autism Brain Imaging Data Exchange (ABIDE) represents a significant repository of brain imaging and phenotypic data collected from nineteen sites, encompassing a total of 1,114 cases of both ASD and typical control individuals. Each case includes 347 descriptive variables. This article demonstrates a case study on detecting ASD based on a machine learning (ML) pipeline utilizing phenotypic data from ABIDE. The ML pipeline involves four primary steps: (1) collecting and integrating data, (2) preprocessing the data, (3) training an ML model, and (4) evaluating the ML model. This article employs seven distinct ML algorithms for training the model and documenting the classification accuracy of each algorithm. During the case study, the accuracy ranged from 80.50% to 95.10%. The model trained using the random forest algorithm achieved the preeminent accuracy for ASD detection using phenotypic data.
Keywords
ASD; Machine Learning; ABIDE; Phenotypic Data
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