Fuzzy Cognitive Map to Classify Plantar Foot Alterations

The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can handle the uncertainties present in biological data using different Artificial Intellig...

Full description

Saved in:
Bibliographic Details
Published in:Revista IEEE América Latina 2022-07, Vol.20 (7), p.1092-2000
Main Authors: Ramirez Bautista, Julian Andres, Chaparro-Cardenas, Silvia L., Hernandez-Zavala, Antonio, Gallegos-Torres, Ruth Magdalena, Zequera, Martha, Tovar-Barrera, Yosabad, Pradilla-Gomez, Juan M., Huerta-Ruelas, Jorge Adalberto
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
bacterial foraging optimization algorithm
Behavioral sciences
Biological system modeling
Classification
Clinical decision support systems
Cognitive maps
Cognitive models
Decision analysis
Decision support systems
Foot
Fuzzy cognitive maps
Medical services
Microorganisms
Optimization
optimization algorithms
Orthopedics
Physicians
plantar data analysis
Plantar pressure
Pressure distribution
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can handle the uncertainties present in biological data using different Artificial Intelligence techniques to obtain accurate and easy-to-use systems. This paper presents the application of a Fuzzy Cognitive Map (FCM) formulation, for knowledge extraction in the classification of human plantar foot alterations, with a relatively small and transparent model. The FCM is trained using the Bacterial Search Optimization Algorithm (BFOA). One hundred and twenty-five volunteer subjects (aged 20-68 years) participated in the study. Classification of the foot into normal (n=31), flat (n=32), cavus type III (n=31) and cavus type IV (n=31) to train the system was performed by specialized physicians. The test was performed by walking on a FreeMed platform. The proposed method shows an accuracy rate of about 89% in the classification task and allows extracting information related to the important factors that the system considers to make a decision.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2021.9827472