INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.14, No.1, Feb. 2022

Adaptive Neuro-Fuzzy Inferential Approach for the Diagnosis of Prostate Diseases

Full Text (PDF, 968KB), PP.32-41


Views:0   Downloads:0

Author(s)

Matthew Cobbinah, Umar Farouk Ibn Abdulrahman, Abaidoo Kwame Emmanuel

Index Terms

Prostate Diseases Diagnosis;Artificial Intelligence;Soft Computing;ANFIS;RMSE

Abstract

In this study, Adaptive Neuro-fuzzy Inferential System (ANFIS) is adapted for diagnosing prostate diseases. The system involves generating and tuning a fuzzy inference system to handle the imprecise terms used for describing prostate cases and severity. Several diagnostic variables were used to learn the feature statistics present in a typical data, while the trained model was validated and adapted for testing new prostate cases. A total of 335 data from patients’ records were collected at the Medi Moses Prostate Centre, Kumasi Ghana. The dataset was partitioned into 70% which was used for model training, and the other 30% was utilized in the validation phase. The proposed model was implemented in the MATLAB environment. Evaluation result from the proposed system demonstrated that the system achieved an accurate diagnostic result with an RMSE value of 11%. This indicates that the system has a relatively high accuracy and could be accepted for prostate diagnosis. Furthermore, the model was able to learn well and generalize the features in the data set, making the proposed ANFIS model suitable for new cases. Performance analysis showed that the ANFIS is well suited for handling the crispy values used in prostate diagnosis; thus, it can be extensively employed in other similar areas of medical diagnosis. 

Cite This Paper

Matthew Cobbinah, Umar Farouk Ibn Abdulrahman, Abaidoo Kwame Emmanuel, "Adaptive Neuro-Fuzzy Inferential Approach for the Diagnosis of Prostate Diseases", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.1, pp.32-41, 2022. DOI: 10.5815/ijisa.2022.01.03

Reference

[1]Bobé Armant, F., Buil Arasanz, M. E., Allué Buil, A. I. & Vila Barja, J. Prostate disease. FMC Form. Medica Contin. en Aten. Primaria 12, 7–28 (2005).

[2]Omisore, M. O., Samuel, O. W. & Atajeromavwo, E. J. A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis. Appl. Comput. Informatics 13, 27–37 (2017).

[3]Jalali Varnamkhasti, M., Lee, L. S., Abu Bakar, M. R. & Leong, W. J. A genetic algorithm with fuzzy crossover operator and probability. Adv. Oper. Res. 2012, 1–17 (2012).

[4]Saritas, I., Allahverdi, N. & Sert, I. U. A fuzzy expert system design for diagnosis of prostate cancer. 345–351 (2009) doi:10.1145/973620.973677.

[5]Omisore O. M., Ojokoh B. A., Babalola A. E., Folajimi Y., and Wang L., “An Affective Learning-based System for Diagnosis and Personalized Management of Diabetes Mellitus”, Future Generation Computer System, Volume 117, April 2021, Pages 273-290.

[6]Omisore O. M., Han S. P., Ren L. X., Wang G. S., Ou F. L., Li H., and Wang L., “Towards Characterization and Adaptive Compensation of Backlash in a Novel Robotic Catheter System for Cardiovascular Intervention”, IEEE Transactions on Biomedical Circuits and Systems, 12(4):824-838, April 2018. 

[7]Koutsojannis, C., Tsimara, M. & Nabil, E. HIROFILOS: A Medical Expert System for Prostate Diseases. Proc. 7th Wseas Int. Conf. Comput. Intell. Man-Machine Syst. Cybern. 254–259 (2008).

[8]Keles, A., Hasiloglu, A. S., Keles, A. & Aksoy, Y. Neuro-fuzzy classification of prostate cancer using NEFCLASS-J. 37, 1617–1628 (2007).

[9]Catto, J. W. Artificial Intelligence in Predicting Bladder Cancer Outcome: A Comparison of Neuro-Fuzzy Modeling and Artificial Neural Networks 1. 9, 4172–4177 (2003).

[10]Hosseini, M. S. & Zekri, M. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System. 20894 (2012).

[11]Çinar, M., Engin, M., Engin, E. Z. & Ziya Ateşçi, Y. Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Expert Syst. Appl. 36, 6357–6361 (2009).

[12]Duodu, Q., Kobina Panford, J. & Ben Hayfron-Acquah, J. Designing Algorithm for Malaria Diagnosis using Fuzzy Logic for Treatment (AMDFLT) in Ghana. Int. J. Comput. Appl. 91, 22–28 (2014).

[13]Post, E. CHAPTER 3 CONCEPTS OF ANN, FUZZY LOGIC AND ANFIS. 36–53 (1936).

[14]Samanta, D. Chapter 5 Defuzzification methods. 1–7 (2001).