Diagnoses Breast Cancer Disease by Using fuzzy artmap
DOI:
https://doi.org/10.65405/.v10i37.689Keywords:
fuzzy artmap, epoch, supervised learning, missing value.fuzzy artmap, epoch, supervised learning, missing value.Abstract
This research concentrated on creating a fuzzy ARTMAP classifier to categorize breast cancer instances. The fuzzy ARTMAP is a neural network family, a component of artificial intelligence structures designed for continuous supervised learning of recognition classes. Deriving knowledge and insights from datasets, meaning data mining, has gained considerable interest within the database sphere to forecast emerging data patterns for undiagnosed patient conditions. This paper introduces a method for diagnosing breast cancer illness using an algorithm encompassing several facets of actual data. Thus, this setup aids pathologists when addressing disease diagnosis, particularly where certain signs of this cancer are not readily apparent. The algorithm offers the chance to utilize trained and tested historical data to construct a framework. The software's outcome proves accurate for analysts to support them in clinical decision-making tools.
Downloads
References
[1] Liaw, L. C. M., Tan, S. C., Goh, P. Y., & Lim, C. P. (2024). Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP. Neural Computing and Applications, 36(22), 13895-13913.
[2] Mohanty, M. N., & Das, A. (2024). Skin cancer detection from dermatoscopic images using hybrid fuzzy ensemble learning model. International Journal of Fuzzy Systems, 26(1), 260-273.
[3] Das, A., & Mohanty, M. N. (2022). Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection. Applied Computing and Informatics.
[3] Abd Karim, S. A., Mohamad, U. H., & Nohuddin, P. N. E. (2025). Interaction-Based Feature Selection Technique Using Fuzzy Discretization and Class Association Rule Mining for Breast Cancer Classification. Malaysian Journal of Fundamental and Applied Sciences, 21(2), 1832-1859.
[3] Lai, W. C., & Srividhya, S. R. (2022). A modified LBP operator-based optimized fuzzy art map medical image retrieval system for disease diagnosis and prediction. Biomedicines, 10(10), 2438.
[3] Akalın, F., & Yumuşak, N. (2023). Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network- Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 23(4), 941-954
[3] Abd Karim, S. A., Mohamad, U. H., & Nohuddin, P. N. E. (2025). Interaction-Based Feature Selection Technique Using Fuzzy Discretization and Class Association Rule Mining for Breast Cancer Classification. Malaysian Journal of Fundamental and Applied Sciences, 21(2), 1832-1859.
[4] Melton, N. M., da Silva, L. E. B., & Wunsch, D. C. (2025, March). An extensive analysis of match-tracking methods for ARTMAP. In 2025 IEEE Symposium on Computational Intelligence in Health and Medicine (CIHM) (pp. 1-8). IEEE.
[5] Yadav, A., Dwivedi, S., Dwivedi, A., Thakur, U., & Akhtar, D. N. (2025). Intelligent Disease Diagnosis: A Multi-Disease Prediction Approach Using Machine Learning. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN, 2395-1990.
[6] Prusty, C. A. K., Patra, A., Ranjan, A. K., Kumar, R., & Das, A. (2025, January). Plant Disease Detection from Leaf
Images Utilizing Ensemble Convolutional Neural Network. In 2025 International Conference on Ambient Intelligence inHealth Care (ICAIHC) (pp. 1-6). IEEE.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
