Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K‐means clustering and labeled ensemble classifier algorithm

• Published in: Concurrency and computation Vol. 34; no. 10
• Main Authors: K, Srikanth, Zahoor Ul Huq, S., Siva Kumar, A. P.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.05.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Big Data; Breast cancer; Classification; Classifiers; Clustering; Data analysis; Data mining; Diagnosis; ensemble classifier; Heuristic methods; Image manipulation; K‐means clustering; Machine learning; Mathematical models; Optimization; swarm intelligence algorithm
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6715

Breast cancer is a very dangerous disease that mainly affects women. It is a deadliest disease that highly affects the women's life. Therefore, it is necessary to predict and classify this deadly disease for early diagnosis. There exist numerous data mining techniques for early prediction and classification of this disease. The big data based analytical model provides the better solution for storing, manipulating, and analyzing a great number of mammographic images. In this article, a new improved fractional rough fuzzy K‐means clustering strategy is considered for disease prediction. Then, a new Tunicate Swarm Algorithm (TSA) is introduced to optimize the weight parameters. TSA is a bio‐inspired metaheuristic optimization approach. Finally, the labeled ensemble classifier (LEC) is utilized for classifying the stages of breast cancer as malignant and benign. Here, the data is randomly generated from breast cancer Wisconsin dataset (diagnosis) obtainable on UCI machine learning repository. The proposed strategy is compared with different existing strategies, like Logistic Regression Classifier, Random Forest Classifier. From the analysis, it is observed that the proposed big data based analytical model using LEC provides 99.3% accuracy that is very high when compared to the accuracy of existing approaches.