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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44496

Grey Wolf Enabled Intuitionistic Fuzzy CLARANS clustering for Breast Cancer Prediction

G.Sumalatha, N.Kavitha

Abstract

The rapid advancements in the field of medicine greatly facilitates the process of disease diagnosis by integrating information technology, artificial intelligence and machine learning paradigms. While diagnosing the disease, it is very essential to detect them as early as possible, so that there is a greater possibility in treating the diseases, thereby increasing the victim’s chances of recovery from such diseases. From many dreadful diseases identified across the globe, breast cancer seems to be a major health issue with women and no of women affected by it is on the rise in the recent years. There are many empirical models which address the presence of breast cancer using mining approaches and machine learning paradigms. This research work puts forth a method to handle impreciseness and uncertainty in an optimized way by introducing a heuristic swam intelligence to overcome the problem of local optima while clustering the similar patterns in the breast cancer dataset. This research work uses Intuitionistic Fuzzy Clustering Large Applications based upon Randomized Search (CLARANS) Clustering optimized by Grey Wolf Optimization (GWO-IFCL) to select the centroids and fuzzy analytical hierarchy to determine significant features of the breast cancer dataset, which increases the accuracy of clustering with more relevancy. From the obtained results it explores that GWO-IFCL greatly enhances the accuracy of breast cancer detection rate while comparing with other clustering models.

Keywords

breast cancer dataset, uncertainty, vagueness, impreciseness, intuitionistic fuzzy CLARANS, Grey Wolf Optimization

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