Enhancement of Logistic Regression to Detect Breast Cancer using Cross Validation

Many women in the world are suffering from cancer. Breast cancer is among one of the cancer which is affecting women all over the globe. The machine learning algorithms are found good for detecting breast cancers. Logistic Regression is among the best supervised machine learning algorithms which is found very good in detecting breast cancer. Many hospitals provided their data of females which comes for breast cancer detection. WDBC is one of the best known data sets for breast cancer. K-fold cross-validation reduces the possibility of overfitting and enhances generalization by repeating the process across all folds and computing the average performance. By applying the Logistic Regression model with cross validation we can more effectively generalize to new data. Through the identification of the most significant predictors, feature selection further enhances the model by lowering computational complexity and dimensionality. The findings show that good accuracy for breast cancer prediction is obtained when using Logistic Regression in conjunction with cross-validation. The model is a good fit for clinical applications because of its robust performance, ease of interpretation, and simplicity. The findings of this study demonstrate the validity of logistic regression as a tool for the diagnosis of breast cancer and imply that its combination with cross validation improves diagnostic accuracy and aid in clinical decision-making.