Machine Learning Based Optimal Feature Selection for Pediatric Ultrasound Kidney Images Using Binary Coati Optimization
- Title
- Machine Learning Based Optimal Feature Selection for Pediatric Ultrasound Kidney Images Using Binary Coati Optimization
- Creator
- Kausar F.; Ramamurthy B.
- Description
- Chronic kidney disease (CKD) one of the most dangerous illnesses. Early detection is vital for improving survival rates and underscoring the need for an intelligent classifier to differentiate between normal and abnormal kidney ultrasound images. Features extracted from an image have a significant impact on classification accuracy. In this study, we present a Binary Coati optimization algorithm (BCOA) for feature selection in CKD, which focuses on reducing the high dimensionality features extracted from ultrasound images, including GLCM, GLRLM, GLSZM, GLDM, NGTDM, and first order, by employing BCOA-S shaped and BCOA-V shaped transfer functions that convert BCOA from a continuous search space to a binary form, which helps in the selection of optimal features to improve the classification performance while reducing the feature dimensionality. The reduced feature was evaluated using six machine-learning classifiers: Random Forest, Support Vector Machine, Decision tree, K-nearest Neighbor, XG-boost, and Nae Bayes. The efficiency of the proposed framework was assessed based on accuracy, precision, recall, specificity, f1 score and AUC curve. BCOA-V outperformed in terms of accuracy, precision, recall, specificity, F1 score and AUC curve by 99%,100%,97%,100%, 98%, and 98%, respectively. This makes it a superior choice for CKD diagnosis and is a valuable tool for feature selection in medical diagnosis. (2024), (Intelligent Network and Systems Society). All rights reserved.
- Source
- International Journal of Intelligent Engineering and Systems, Vol-17, No. 6, pp. 1300-1313.
- Date
- 2024-01-01
- Publisher
- Intelligent Network and Systems Society
- Subject
- Binary coati optimisation algorithm (BCOA); Chronic kidney disease (CKD); Feature selection (FS); Optimisation algorithm; Texture
- Coverage
- Kausar F., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Ramamurthy B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India
- Rights
- Restricted Access
- Relation
- ISSN: 2185310X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Kausar F.; Ramamurthy B., “Machine Learning Based Optimal Feature Selection for Pediatric Ultrasound Kidney Images Using Binary Coati Optimization,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/13434.