Breast Cancer Prediction via Grid Search Hyperparameter Optimization
Keywords:
Breast Cancer, early detection, machine learning, hyperparameter optimization, logistic regression, support vector machine, k-nearest neighbor, naive bayes, decision tree, random forest, OWBCD, WDBC, Coimbra, BRCA, Haberman, SEER.Abstract
Breast Cancer is a serious health issue worldwide, and early detection is crucial in preventing deaths. Machine learning can help identify tumors efficiently, and this paper introduces the Grid Search Hyperparameter Optimization (GSHPO) method to optimize the parameters of six existing models, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest. The best parameters were applied to predict outcomes in six datasets, including OWBCD, WDBC, Coimbra, BRCA, Haberman, and SEER. The results show that tuning the hyperparameters of models has a significant positive impact on prediction accuracy.
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