Improving Diagnosis of Breast Cancer Disease Using Adaptive Neuro-Fuzzy Inference System

Document Type : Original Article

Authors

1 Assistant Professor, Department of Computer Engineering, Faculty of Technical and Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.

2 Faculty member, Department of Computer Engineering, Torbat Heydariyeh Branch, Islamic Azad University, Torbat Heydariyeh, Iran.

3 M.Sc, Department of Computer Engineering, Faculty of Technical and Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.

4 PhD Student, Department of Computer Engineering, Faculty of Technical and Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.

Abstract

Cancer and particularly breast cancer is one of the most common diseases among women worldwide. Early detection of breast cancer is a major challenge for physicians and is key in successful treatment and patient survival. This study introduces some data mining methods for the prediction of breast cancer based on a dataset containing 683 independent records with 9 features from the UCI machine learning repository. The models were used to diagnose benign and malignant breast cancer. Results showed that the accuracy of Multi-Layer Perceptron Neural Network (MLP), Learning Vector Quantization (LVQ) Neural Network, Radial Basis Function (RBF), Fuzzy Clustering (KFC), Adaptive Neuro-Fuzzy Inference System Model (ANFIS) were 97.5%, 97.5%, 98.3%, 75% and 99.2%, respectively. Early diagnosis of breast cancer disease reduces the cost of treatment and increases the chance of successful treatment. This study demonstrated that neuro-fuzzy inference system performed better than other models for breast cancer diagnosis. In this study, while diagnosing breast cancer, it was illustrated that models based on fuzzy neural inference had a more acceptable performance than other methods in diagnosing breast cancer. The proposed model can assist the medical community, particularly mammography specialists.

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Main Subjects

References

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Karafan Quarterly Scientific Journal
Volume 19, Issue 3 - Serial Number 59
Technical and Engineering
December 2022
Pages 377-391

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History

  • Receive Date: 31 May 2021
  • Revise Date: 31 August 2021
  • Accept Date: 03 January 2022

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