Fuzzy Cognitive Map to Classify Plantar Foot Alterations

Authors

  • Julian Andres Ramirez Bautista Fundación Universitaria de San Gil - UNISANGIL https://orcid.org/0000-0002-6472-5751
  • Silvia L. Chaparro-Cárdenas Research Department of Fundación Universitaria de San Gil – UNISANGIL, Santander, Colombia https://orcid.org/0000-0002-2589-259X
  • Antonio Hernández-Zavala Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada – Instituto Politécnico Nacional, Av. Cerro Blanco #141, Col. Colinas del Cimatario, Querétaro, México https://orcid.org/0000-0002-0964-9522
  • Ruth Magdalena Gallegos-Torres Department of Nursing, Universidad Autónoma de Querétaro, Centro Universitario, 76010, Querétaro, México https://orcid.org/0000-0001-8034-4089
  • Martha Zequera Departamento de Electrónica, BASPI-FootLaB, Pontificia Universidad Javeriana, Cra. 7 No. 40-62, Bogotá, Colombia https://orcid.org/0000-0001-8226-3370
  • Yosabad Tovar-Barrera Yosafisio Clinic of Querétaro, México https://orcid.org/0000-0002-1743-5671
  • Juan M. Pradilla-Gómez School of Medicine, Pontificia Universidad Javeriana, Cra 7 No. 40–62, Bogotá, Colombia https://orcid.org/0000-0002-4423-1623
  • Jorge Adalberto Huerta-Ruelas Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada – Instituto Politécnico Nacional, Av. Cerro Blanco #141, Col. Colinas del Cimatario, Querétaro, México https://orcid.org/0000-0001-5632-3368

Keywords:

Clinical decision support systems, bacterial foraging optimization algorithm, fuzzy cognitive maps, optimization algorithms, plantar data analysis

Abstract

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.

Downloads

Download data is not yet available.

References

J. A. R. Bautista, S. L. C. Cárdenas, A. H. Zavala, and J. A. Huerta-Ruelas, “Review on plantar data analysis for disease diagnosis,” Biocybern. Biomed. Eng., vol. 8, 2018.

J. A. Ramirez-Bautista, J. A. Huerta-Ruelas, S. L. Chaparro-Cardenas, and A. Hernandez-Zavala, “A Review in Detection and Monitoring Gait Disorders Using In-Shoe Plantar Measurement Systems,” IEEE Rev. Biomed. Eng., vol. 10, no. c, pp. 299–309, 2017.

S. Kundu, U. S. Acharya, and S. Mukherjee, Proceedings of the 2nd International Conference on Communication, Devices and Computing, vol. 602. Singapore: Springer Singapore, 2020.

D. R. Bonanno, K. Ledchumanasarma, K. B. Landorf, S. E. Munteanu, G. S. Murley, and H. B. Menz, “Effects of a contoured foot orthosis and flat insole on plantar pressure and tibial acceleration while walking in defence boots,” Sci. Rep., vol. 9, no. 1, p. 1688, Dec. 2019.

J. A. Ramirez-Bautista, J. A. Huerta-Ruelas, S. L. Chaparro-Cárdenas, and A. Hernández-Zavala, “A Review in Detection and Monitoring Gait Disorders Using In-Shoe Plantar Measurement Systems,” IEEE Reviews in Biomedical Engineering, vol. 10. pp. 299–309, 2017.

J. Chae, Y.-J. Kang, and Y. Noh, “A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data,” Sensors, vol. 20, no. 16, p. 4481, Aug. 2020.

D. A. Pelta, C. Cruz, A. D. Masegosa, E. Onieva, P. Lopez-garcia, and E. Osaba, Soft Computing Based Optimization and Decision Models, Springer I., vol. 360. Cham: Springer International Publishing, 2018.

S. Sujamol, S. Ashok, and U. K. Kumar, “Study of Fuzzy Cognitive Maps for Modeling Clinical Support Systems,” Int. J. Pure Appl. Math., vol. 119, no. 12, pp. 15433–15445, 2018.

R. Acharya U et al., “Automated Identification of Diabetic Type 2 Subjects with and without Neuropathy Using Wavelet Transform on Pedobarograph,” J. Med. Syst., vol. 32, no. 1, pp. 21–29, Feb. 2008.

H. Li et al., “Modified Weights-and-Structure-Determination Neural Network for Pattern Classification of Flatfoot,” IEEE Access, vol. 7, pp. 63146–63154, 2019.

S. Xu, X. Zhou, and Y.-N. Sun, “A novel gait analysis system based on adaptive neuro-fuzzy inference system,” Expert Syst. Appl., vol. 37, no. 2, pp. 1265–1269, Mar. 2010.

Z. Mei, K. Ivanov, G. Zhao, Y. Wu, M. Liu, and L. Wang, “Foot type classification using sensor-enabled footwear and 1D-CNN,” Meas. J. Int. Meas. Confed., vol. 165, p. 108184, Dec. 2020.

J. Han, D. Wang, Z. Li, N. Dey, R. G. Crespo, and F. Shi, “Plantar pressure image classification employing residual-network model-based conditional generative adversarial networks: a comparison of normal, planus, and talipes equinovarus feet,” Soft Comput., pp. 1–20, Aug. 2021.

D. A. Pelta and C. Cruz, "Soft Computing Based Optimization and Decision Models", Springer I., vol. 360. Cham: Springer International Publishing, 2018.

E. Vayena, A. Blasimme, and I. G. Cohen, “Machine learning in medicine: Addressing ethical challenges,” PLOS Med., vol. 15, no. 11, p. e1002689, Nov. 2018.

A. Amirkhani, E. I. Papageorgiou, A. Mohseni, and M. R. Mosavi, “A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications,” Comput. Methods Programs Biomed., vol. 142, pp. 129–145, Apr. 2017.

E. I. Papageorgiou and J. L. Salmeron, “A Review of Fuzzy Cognitive Maps Research During the Last Decade,” IEEE Trans. Fuzzy Syst., vol. 21, no. 1, pp. 66–79, Feb. 2013.

C. D. Stylios and P. P. Groumpos, “Mathematical formulation of fuzzy cognitive maps,” Proc. 7th Mediterr. Conf. Control Autom., June 1999, pp. 2251–2261.

G. Papakostas and D. E. Koulouriotis, “Classifying Patterns Using Fuzzy Cognitive Maps,” in Fuzzy cognitive maps, 2010, vol. 247, pp. 291–306.

S. Ahmadi, N. Forouzideh, C. H. Yeh, R. Martin, and E. Papageorgiou, “A first study of Fuzzy Cognitive Maps learning using cultural algorithm,” in Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014, 2014, pp. 2023–2028.

C. D. Stylios, V. C. Georgopoulos, G. A. Malandraki, and S. Chouliara, “Fuzzy cognitive map architectures for medical decision support systems,” Appl. Soft Comput., vol. 8, no. 3, pp. 1243–1251, Jun. 2008.

P. P. Groumpos and A. P. Anninou, “A theoretical mathematical modeling of Parkinson’s disease using Fuzzy Cognitive Maps,” in 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), Nov. 2012, no. November, pp. 677–682.

E. I. Papageorgiou, C. D. Stylios, and P. P. Groumpos, “An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps,” IEEE Trans. Biomed. Eng., vol. 50, no. 12, pp. 1326–1339, Dec. 2003

P. Oikonomou and E. I. Papageorgiou, “Particle Swarm Optimization Approach for Fuzzy Cognitive Maps Applied to Autism Classification,” in IFIP Advances in Information and Communication Technology, vol. 412, IFIP International Federation for Information Processing, 2013, pp. 516–526.

M. Khodadadi, H. Shayanfar, K. Maghooli, and A. H. Mazinan, “Prediction of stroke probability occurrence based on fuzzy cognitive maps,” Automatika, vol. 60, no. 4, pp. 385–392, Oct. 2019.

V. K. Mago, R. Mehta, R. Woolrych, and E. I. Papageorgiou, “Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping,” BMC Med. Inform. Decis. Mak., vol. 12, no. 1, p. 98, Dec. 2012.

J. A. Ramirez-Bautista, J. A. Huerta-Ruelas, L. T. Kóczy, M. F. Hatwágner, S. L. Chaparro-Cárdenas, and A. Hernández-Zavala, “Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network,” Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 404–414, 2020.

B. Kosko, “Fuzzy cognitive maps,” Int. J. Man. Mach. Stud., vol. 24, no. 1, pp. 65–75, Jan. 1986.

G. A. Papakostas, Y. S. Boutalis, D. E. Koulouriotis, and B. G. Mertzios, “Fuzzy cognitive maps for pattern recognition applications,” Int. J. Pattern Recognit. Artif. Intell., vol. 22, no. 8, pp. 1461–1486, Dec. 2008.

M. F. Hatwágner, V. A. Niskanen, and L. T. Kóczy, “Behavioral analysis of fuzzy cognitive map models by simulation,” IFSA-SCIS 2017 - Jt. 17th World Congr. Int. Fuzzy Syst. Assoc. 9th Int. Conf. Soft Comput. Intell. Syst., 2017.

G. Nápoles et al., “Fuzzy cognitive modeling: Theoretical and practical considerations,” in Smart Innovation, Systems and Technologies, vol. 142, no. June, 2019, pp. 77–87.

M. F. Hatwágner, G. Vastag, V. A. Niskanen, and L. T. Kóczy, “Banking applications of FCM models,” Springer-Verlag, pp. 1–9, 2011.

E. I. Papageorgiou, “Learning Algorithms for Fuzzy Cognitive Maps—A Review Study,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 42, no. 2, pp. 150–163, Mar. 2012.

G. Felix, G. Nápoles, R. Falcon, W. Froelich, K. Vanhoof, and R. Bello, “A review on methods and software for fuzzy cognitive maps,” Artif. Intell. Rev., vol. 52, no. 3, pp. 1707–1737, Oct. 2019.

G. A. Papakostas, D. E. Koulouriotis, A. S. Polydoros, and V. D. Tourassis, “Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems,” Expert Syst. Appl., vol. 39, no. 12, pp. 10620–10629, Sep. 2012.

E. I. Papageorgiou, C. D. Stylios, and P. P. Groumpos, “Active Hebbian learning algorithm to train fuzzy cognitive maps,” Int. J. Approx. Reason., vol. 37, no. 3, pp. 219–249, Nov. 2004.

C. D. Stylios and V. C. Georgopoulos, “Genetic algorithm enhanced Fuzzy Cognitive Maps for medical diagnosis,” in 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), Jun. 2008, pp. 2123–2128.

K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Syst., vol. 22, no. 3, pp. 52–67, Jun. 2002.

B. Hernandez-Ocana, E. Mezura-Montes, and P. Pozos-Parra, “A review of the bacterial foraging algorithm in constrained numerical optimization,” 2013 IEEE Congr. Evol. Comput. CEC 2013, pp. 2695–2702, 2013.

K. M. Passino, “Bacterial Foraging Optimization,” Int. J. Swarm Intell. Res., vol. 1, no. 1, pp. 1–16, Jan. 2010.

N. K. Jhankal and D. Adhyaru, “Comparative Analysis of Bacterial Foraging Optimization Algorithm with Simulated Annealing,” Int. J. Sci. Res., vol. 3, no. 3, pp. 10–13, 2014.

S. Das, A. Biswas, S. Dasgupta, and A. Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications,” pp. 23–55, 2009.

Published

2022-04-27

How to Cite

Ramirez Bautista, J. A., Chaparro-Cárdenas, S. L., Hernández-Zavala, A., Gallegos-Torres, R. M., Zequera, M., Tovar-Barrera, Y., Pradilla-Gómez, J. M., & Huerta-Ruelas, J. A. (2022). Fuzzy Cognitive Map to Classify Plantar Foot Alterations. IEEE Latin America Transactions, 100(XXX). Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6319