Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning

Mukesh Kumar, Saurabh Singhal, Shashi Shekhar, Bhisham Sharma and Gautam Srivastava ()
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Mukesh Kumar: School of Computer Application, Lovely Professional University, Phagwara 144402, India
Saurabh Singhal: Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
Shashi Shekhar: Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
Bhisham Sharma: Department of Computer Science & Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Baddi 174103, India
Gautam Srivastava: Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada

Sustainability, 2022, vol. 14, issue 21, 1-26

Abstract: Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.

Keywords: ensemble learning; stacking; classification; optimization; breast cancer; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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