| Title: |
Ataxia severity classification using enhanced feature selection and ranking optimization through machine learning model |
| Authors: |
Durganivas Seetharama, Pavithra; Math, Shrishail |
| Source: |
Indonesian Journal of Electrical Engineering and Computer Science; Vol 32, No 3: December 2023; 1605-1613 ; 2502-4760 ; 2502-4752 ; 10.11591/ijeecs.v32.i3 |
| Publisher Information: |
Institute of Advanced Engineering and Science |
| Publication Year: |
2023 |
| Subject Terms: |
Ataxic severity classification; Class imbalance; Deep learning; Feature extraction-selection; Machine learning; Multi-label classification |
| Description: |
The examination of neurological disorders and the monitoring of ataxic gait are major scientific topics that benefit from digital signal processing techniques and machine learning (ML) technologies. In this research, an ML approach is optimized with the use of Spatio-temporal data obtained from a kinect-sensor to differentiate between normal gait and ataxic. The current ML-based approaches perform very poorly because they cannot build feature-correlation among many gait characteristics. Furthermore, current ML-based techniques generate more false-positive whenever data is imbalanced in nature; especially for performing multi-label classification. This work presents a feature selection and ranking (FSR) based on extreme gradient boost (XGB) for ataxia severity classification. The FSR-XGB introduce an enhanced misclassification minimization error optimization and presents a novel feature selection and ranking to introduce feature importance using new cross-validation mechanism, both of which are aimed at solving the multi-label classification research problems. Results from experiments demonstrate that the presented FSR-XGB approach outperforms other ML-based and deep learning-based approaches. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://ijeecs.iaescore.com/index.php/IJEECS/article/view/32478/17803; https://ijeecs.iaescore.com/index.php/IJEECS/article/view/32478 |
| DOI: |
10.11591/ijeecs.v32.i3.pp1605-1613 |
| Availability: |
https://ijeecs.iaescore.com/index.php/IJEECS/article/view/32478; https://doi.org/10.11591/ijeecs.v32.i3.pp1605-1613 |
| Rights: |
Copyright (c) 2023 Institute of Advanced Engineering and Science ; http://creativecommons.org/licenses/by-nc/4.0 |
| Accession Number: |
edsbas.25700D33 |
| Database: |
BASE |