An Optimized Framework Based on Data Exploration and Dynamic Ensemble-Based Models for Breast Cancer Prediction
DOI:
https://doi.org/10.47839/ijc.23.2.3544Keywords:
Data Exploration, Ensemble Classifier, Hyperparameters Tuning, Machine LearningAbstract
Breast cancer (BC) is a major global health concern. Detecting BC at an early stage gives more treatment options and can help avoid more aggressive treatments. The use of machine learning (ML) in BC prediction offers significant potential for improving the accuracy and speed of diagnosis, personalizing treatment, and identifying high-risk patients. However, there are significant challenges associated with the use of ML, including the need for high-quality data and more flexible models with optimal parameters to achieve high efficiency. In this paper, we propose an optimized framework based on multi-stage data exploration. This framework is designed to provide a comprehensive approach to data exploration, ensuring that the data is well-prepared for ML. In addition, the framework includes dynamic ensemble-based classifiers, which combine multiple independent classifiers to improve accuracy and mitigate the risk of overfitting in conjunction with the cross-validation techniques. These classifiers are optimized using Bayesian hyperparameter tuning, which involves selecting the optimal values for the various hyperparameters of the model. This approach can significantly improve the prediction accuracy of the resulting model. The study evaluates the framework using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) dataset and compares our results with other state-of-the-art models. The experimental results show that the best result is 100% for accuracy and recall with hyperparameters of (Ensemble Method = AdaBoost, Number of learners = 322, learning rate = 0.9350, and the Maximum number of splits = 1). The highest performance has been achieved with the proposed framework compared with the other models in terms of accuracy (mean = 99.35%, best = 100%, worst = 98.7%, and Standard Deviation = 0.325). The framework can potentially improve the accuracy and efficiency of BC prediction, ultimately leading to better outcomes for patients.
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