Article: AdaCluCSL: an approach for autism spectrum disorder prediction using adaptive clustering smote and cost-sensitive learning Journal: International Journal of Computational Biology and Drug Design (IJCBDD) 2025 Vol.16 No.3 pp.185 - 211 Abstract: Machine learning struggles to predict autism spectrum disorder (ASD) due to real-world datasets' underlying class imbalance. Adaptive cluster SMOTE and cost-sensitive learning (AdaCluCSL) is a new approach that improves ASD prediction accuracy. Combining adaptive k-means clustering SMOTE with cost-sensitive learning achieves this. Because positive ASD samples are few, conventional classification methods sometimes have poor predictive accuracy. AdaCluCSL uses adaptive clustering to find complex clusters in the underrepresented class and address this disparity. It then creates synthetic samples using SMOTE with cluster-specific weights. Then, the enriched samples are used with the original cases to train a cost-sensitive classifier that allocates additional costs to minority class misclassifications. Experimental tests on benchmark ASD datasets show that AdaCluCSL improves ASD prediction accuracy. Precision and recall rates have improved significantly, outperforming traditional methods. The AdaCluCSL algorithm can advance ASD prediction. It solves uneven data distribution issues, making early diagnosis and intervention more reliable. Inderscience Publishers - linking academia, business and industry through research

Title: AdaCluCSL: an approach for autism spectrum disorder prediction using adaptive clustering smote and cost-sensitive learning

Authors: M. Kavitha; M. Kasthuri

Addresses: Department of Computer Science, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli-620017, Tamil Nadu, India ' Department of Computer Science, Bishop Heber College, Affiliated to Bharathidasan University, Tiruchirappalli-620017, Tamil Nadu, India

Abstract: Machine learning struggles to predict autism spectrum disorder (ASD) due to real-world datasets' underlying class imbalance. Adaptive cluster SMOTE and cost-sensitive learning (AdaCluCSL) is a new approach that improves ASD prediction accuracy. Combining adaptive k-means clustering SMOTE with cost-sensitive learning achieves this. Because positive ASD samples are few, conventional classification methods sometimes have poor predictive accuracy. AdaCluCSL uses adaptive clustering to find complex clusters in the underrepresented class and address this disparity. It then creates synthetic samples using SMOTE with cluster-specific weights. Then, the enriched samples are used with the original cases to train a cost-sensitive classifier that allocates additional costs to minority class misclassifications. Experimental tests on benchmark ASD datasets show that AdaCluCSL improves ASD prediction accuracy. Precision and recall rates have improved significantly, outperforming traditional methods. The AdaCluCSL algorithm can advance ASD prediction. It solves uneven data distribution issues, making early diagnosis and intervention more reliable.

Keywords: prediction of autism spectrum disorder (ASD); K-means cluster; SMOTE; synthetic minority oversampling technique; CSL; cost-sensitive learning; imbalance classification; adaptive cluster SMOTE; cost-sensitive learning (AdaCluCSL).

DOI: 10.1504/IJCBDD.2025.146187

International Journal of Computational Biology and Drug Design, 2025 Vol.16 No.3, pp.185 - 211

Received: 21 Nov 2023
Accepted: 23 Aug 2024
Published online: 09 May 2025 *

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Title: AdaCluCSL: an approach for autism spectrum disorder prediction using adaptive clustering smote and cost-sensitive learning

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