An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm. (December 2022)
- Record Type:
- Journal Article
- Title:
- An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm. (December 2022)
- Main Title:
- An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm
- Authors:
- Pan, Xiaotian
Zhang, Guodao
Lin, Aiju
Guan, Xiaochun
Chen, PingKuo
Ge, Yisu
Chen, Xin - Abstract:
- Abstract: Foot & ankle deformity is a chronic disease with high incidence and is best treated in childhood. However, the current diagnostic procedures rely on doctor's consultation and empirical judgment, and lack objective and quantitative evaluation methods, resulting in low screening rates. To solve this problem, this paper aims to construct an evaluation model for children's foot & ankle deformity through data mining and machine learning technologies. Firstly, it proposes the grading rules for children's foot & ankle deformity severity based on analyzing the existing quantitative indexes and expert experience. Then the 3D foot scanner is used to collect the sample data including 30 foot structure indexes. Finally, an advanced sparse multi-objective evolutionary algorithm (sparse MO-FS) is present for feature selection. The effectiveness of the proposed sparse MO-FS and its search efficiency are proved by comparing 8 feature selection methods and 7 search strategies. Using sparse MO-FS, foot length, arch index, ankle index, and hallux valgus index are selected, which not only simplifies the evaluation model but also improves the average classification accuracy of random forest to more than 98%. Highlights: A grading rules of the severity of children's foot & ankle deformity are proposed. An advanced sparse multi-objective feature selection method is presented. An evaluation model for children's foot & ankle deformity severity is constructed. The model can be used for theAbstract: Foot & ankle deformity is a chronic disease with high incidence and is best treated in childhood. However, the current diagnostic procedures rely on doctor's consultation and empirical judgment, and lack objective and quantitative evaluation methods, resulting in low screening rates. To solve this problem, this paper aims to construct an evaluation model for children's foot & ankle deformity through data mining and machine learning technologies. Firstly, it proposes the grading rules for children's foot & ankle deformity severity based on analyzing the existing quantitative indexes and expert experience. Then the 3D foot scanner is used to collect the sample data including 30 foot structure indexes. Finally, an advanced sparse multi-objective evolutionary algorithm (sparse MO-FS) is present for feature selection. The effectiveness of the proposed sparse MO-FS and its search efficiency are proved by comparing 8 feature selection methods and 7 search strategies. Using sparse MO-FS, foot length, arch index, ankle index, and hallux valgus index are selected, which not only simplifies the evaluation model but also improves the average classification accuracy of random forest to more than 98%. Highlights: A grading rules of the severity of children's foot & ankle deformity are proposed. An advanced sparse multi-objective feature selection method is presented. An evaluation model for children's foot & ankle deformity severity is constructed. The model can be used for the 3D foot scanner to generate an evaluation result. … (more)
- Is Part Of:
- Computers in biology and medicine. Volume 151:Part A(2022)
- Journal:
- Computers in biology and medicine
- Issue:
- Volume 151:Part A(2022)
- Issue Display:
- Volume 151, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 151
- Issue:
- 2022
- Issue Sort Value:
- 2022-0151-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-12
- Subjects:
- Data mining -- Medical diagnosis -- Machine learning -- Evolutionary algorithm -- Foot & ankle deformity
Medicine -- Data processing -- Periodicals
Biology -- Data processing -- Periodicals
610.285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00104825/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compbiomed.2022.106229 ↗
- Languages:
- English
- ISSNs:
- 0010-4825
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.880000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24579.xml