Streamlining Machine Learning Model Execution Time with Hyperparameter Optimization

Breast cancer is a common disease that mostly affects women. It can be fatal if not detected early. Early detection and accurate diagnosis can lead to a longer survival rate for patients. Various classification models have been used to identify breast cancer, including logistic regression, decision trees, random forests, k-nearest neighbors, and gradient-boosting algorithms. A Parameterized Random Forest (PRF) classifier has been proposed that chooses the best parameter for classification based on the highest probability of a particular value in the parameters. The proposed model achieves an accuracy of 98.25% with a false negative rate of 1%. It is also more accurate (95.31%) than other classification algorithms when applied to the Hepatitis C dataset. The PRF classifier is a promising new tool for the early detection and accurate diagnosis of breast cancer. It is more accurate than other classification algorithms and can be used to reduce the time required to choose the best parameter value.