Innovative Approach to Detecting Autism Spectrum Disorder Using Explainable Features and Smart Web Application

Mohammad Abu Tareq Rony, Fatama Tuz Johora, Nisrean Thalji, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin () and Seung Won Lee ()
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Mohammad Abu Tareq Rony: Department of Statistics, Noakhali Science & Technology University, Noakhali 3814, Bangladesh
Fatama Tuz Johora: Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh
Nisrean Thalji: Faculty of Computer Studies, Arab Open University, Amman 11953, Jordan
Ali Raza: Department of Software Engineering, University of Lahore, Lahore 54000, Pakistan
Norma Latif Fitriyani: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Muhammad Syafrudin: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Seung Won Lee: Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea

Mathematics, 2024, vol. 12, issue 22, 1-27

Abstract: Autism Spectrum Disorder (ASD) is a complex developmental condition marked by challenges in social interaction, communication, and behavior, often involving restricted interests and repetitive actions. The diversity in symptoms and skill profiles across individuals creates a diagnostic landscape that requires a multifaceted approach for accurate understanding and intervention. This study employed advanced machine-learning techniques to enhance the accuracy and reliability of ASD diagnosis. We used a standard dataset comprising 1054 patient samples and 20 variables. The research methodology involved rigorous preprocessing, including selecting key variables through data mining (DM) visualization techniques including Chi-Square tests, analysis of variance, and correlation analysis, along with outlier removal to ensure robust model performance. The proposed DM and logistic regression (LR) with Shapley Additive exPlanations (DMLRS) model achieved the highest accuracy at 99%, outperforming state-of-the-art methods. eXplainable artificial intelligence was incorporated using Shapley Additive exPlanations to enhance interpretability. The model was compared with other approaches, including XGBoost, Deep Models with Residual Connections and Ensemble (DMRCE), and fast lightweight automated machine learning systems. Each method was fine-tuned, and performance was verified using k-fold cross-validation. In addition, a real-time web application was developed that integrates the DMLRS model with the Django framework for ASD diagnosis. This app represents a significant advancement in medical informatics, offering a practical, user-friendly, and innovative solution for early detection and diagnosis.

Keywords: autism; data mining; machine learning; ANOVA; logistics regression; web app (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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