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

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.

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

“…Several other methods were also introduced to classify breast cancer [ 34 ]. Few other studies also existed in the literature that proposed deep learning-based frameworks for breast cancer classification, such as the optimized stacking learning approach [ 35 ], fuzzy c-mean and median support value-based CNN approach [ 36 ], and named a few more [ 37 ].…”

Assessment of Carbon Fiber Reinforced Plastic (CFRP) composites Using COPRAS Method

“…Under these four study conditions, hot-wet Test specimens deployed in the atmosphere, increased compared to untreated samples Deboning and exhibiting matrix cracking. CFRP composites are absorbed by the matrix the amount of moisture increases cracking and tensile strength it clearly states that reduces [20].…”

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms

“…Their results indicated that the Logistic Regression model achieved 98% accuracy, surpassing the performance of random forest, decision tree, and K-NN algorithms. Kumar et al 12 developed an optimized stacked integrated learning (OSEL) model for early breast cancer prediction utilizing a dataset from the University of California Irvine repository. Their approach demonstrated higher accuracy compared to individual machine learning prediction models such as AdaBoostM1, Gradient Boosting, Stochastic Gradient Boosting, CatBoost, and XGBoost.…”