A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders
Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective fram...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9999443/ |
| _version_ | 1797904586421305344 |
|---|---|
| author | S. M. Mahedy Hasan Md Palash Uddin Md Al Mamun Muhammad Imran Sharif Anwaar Ulhaq Govind Krishnamoorthy |
| author_facet | S. M. Mahedy Hasan Md Palash Uddin Md Al Mamun Muhammad Imran Sharif Anwaar Ulhaq Govind Krishnamoorthy |
| author_sort | S. M. Mahedy Hasan |
| collection | DOAJ |
| description | Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD. |
| first_indexed | 2024-04-10T09:51:19Z |
| format | Article |
| id | doaj.art-b778e1550a464cc7ba2219851060487b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:51:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b778e1550a464cc7ba2219851060487b2023-02-17T00:00:42ZengIEEEIEEE Access2169-35362023-01-0111150381505710.1109/ACCESS.2022.32324909999443A Machine Learning Framework for Early-Stage Detection of Autism Spectrum DisordersS. M. Mahedy Hasan0Md Palash Uddin1https://orcid.org/0000-0002-4429-6590Md Al Mamun2Muhammad Imran Sharif3Anwaar Ulhaq4https://orcid.org/0000-0002-5145-7276Govind Krishnamoorthy5Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, BangladeshDepartment of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Punjab, PakistanSchool of Computing, Mathematics and Engineering, Charles Sturt University, Port Macquarie, NSW, AustraliaSchool of Psychology and Wellbeing, University of Southern Queensland, Ipswich, QLD, AustraliaAutism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.https://ieeexplore.ieee.org/document/9999443/Autism spectrum disordermachine learningclassificationfeature scalingfeature selection technique |
| spellingShingle | S. M. Mahedy Hasan Md Palash Uddin Md Al Mamun Muhammad Imran Sharif Anwaar Ulhaq Govind Krishnamoorthy A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders IEEE Access Autism spectrum disorder machine learning classification feature scaling feature selection technique |
| title | A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders |
| title_full | A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders |
| title_fullStr | A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders |
| title_full_unstemmed | A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders |
| title_short | A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders |
| title_sort | machine learning framework for early stage detection of autism spectrum disorders |
| topic | Autism spectrum disorder machine learning classification feature scaling feature selection technique |
| url | https://ieeexplore.ieee.org/document/9999443/ |
| work_keys_str_mv | AT smmahedyhasan amachinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT mdpalashuddin amachinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT mdalmamun amachinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT muhammadimransharif amachinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT anwaarulhaq amachinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT govindkrishnamoorthy amachinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT smmahedyhasan machinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT mdpalashuddin machinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT mdalmamun machinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT muhammadimransharif machinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT anwaarulhaq machinelearningframeworkforearlystagedetectionofautismspectrumdisorders AT govindkrishnamoorthy machinelearningframeworkforearlystagedetectionofautismspectrumdisorders |