A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning
Efficient and effective medical diagnostic systems are needed for Autism Spectrum Disorder (ASD) detection and treatment. Healthcare specialists generates extensive remarks on patient behavioural assessment, which is time-consuming to process and record. Early detection of ASD means quality life wit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523000783 |
| _version_ | 1827905112470716416 |
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| author | Anita Vikram Shinde Dipti Durgesh Patil |
| author_facet | Anita Vikram Shinde Dipti Durgesh Patil |
| author_sort | Anita Vikram Shinde |
| collection | DOAJ |
| description | Efficient and effective medical diagnostic systems are needed for Autism Spectrum Disorder (ASD) detection and treatment. Healthcare specialists generates extensive remarks on patient behavioural assessment, which is time-consuming to process and record. Early detection of ASD means quality life with the help of appropriate treatment and care. Machine learning models can be utilized to investigate the feasibility of identifying the stated features and evaluating the presence or absence of autism. This study develops a recommender model with multi-classifiers to enhance precision in the prediction of ASD. Various machine learning algorithms are experimented to assess the model’s performance. We show that Decision Trees and Random Forests exhibit improved performance if analyzed with other algorithms with respect to accuracy, precision, recall, and F1-score as evaluation metrics. |
| first_indexed | 2024-03-13T00:37:21Z |
| format | Article |
| id | doaj.art-ff327f7407f540abb0b5a6aa03b8f621 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-13T00:37:21Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-ff327f7407f540abb0b5a6aa03b8f6212023-07-10T04:04:30ZengElsevierHealthcare Analytics2772-44252023-12-014100211A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine LearningAnita Vikram Shinde0Dipti Durgesh Patil1Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India; Department of Computer Engineering, Marathwada MitraMandal’s College of Engineering, Pune, IndiaDepartment of Information Technology, MKSSS’s Cummins College of Engineering for Women, Pune, Savitribai Phule Pune University, India; Corresponding author.Efficient and effective medical diagnostic systems are needed for Autism Spectrum Disorder (ASD) detection and treatment. Healthcare specialists generates extensive remarks on patient behavioural assessment, which is time-consuming to process and record. Early detection of ASD means quality life with the help of appropriate treatment and care. Machine learning models can be utilized to investigate the feasibility of identifying the stated features and evaluating the presence or absence of autism. This study develops a recommender model with multi-classifiers to enhance precision in the prediction of ASD. Various machine learning algorithms are experimented to assess the model’s performance. We show that Decision Trees and Random Forests exhibit improved performance if analyzed with other algorithms with respect to accuracy, precision, recall, and F1-score as evaluation metrics.http://www.sciencedirect.com/science/article/pii/S2772442523000783Machine learningAutism spectrum disorderRecommender systemMulti-classifier approachArtificial Neural NetworkPersonalized recommendations |
| spellingShingle | Anita Vikram Shinde Dipti Durgesh Patil A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning Healthcare Analytics Machine learning Autism spectrum disorder Recommender system Multi-classifier approach Artificial Neural Network Personalized recommendations |
| title | A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning |
| title_full | A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning |
| title_fullStr | A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning |
| title_full_unstemmed | A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning |
| title_short | A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning |
| title_sort | multi classifier based recommender system for early autism spectrum disorder detection using machine learning |
| topic | Machine learning Autism spectrum disorder Recommender system Multi-classifier approach Artificial Neural Network Personalized recommendations |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523000783 |
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