Predicting Breast Cancer Survival: Comparative Analysis of Machine Learning and Deep Learning Models

The forward-looking study is grounded in the comprehensive chronic cancer death data of Surveillance, Epidemiology, and End Results, one of the most remarkable datasets for breast cancer survival rates ever examined. The approach of this study is to divide patients' data under a particular case feature; in this case, we explore patients' survival rate for breast cancer, which is our main goal. This is done by using the T-test, log-rank test, ANOVA test, and chi-square test, as well as linear regression. In the second part, we include everything from the prediction software to building models. Logistic Regression, Decision Tree and Random Forest Support Vector Machines (S.K.), K-Nearest Neighbors, Naive Bayes, Gradient Boosting, Convolutional Neural Networks, and Long Short-Term Memory Networks Models are heavily evaluated based on various key metrics such as accuracy, precision, and recall. Parameter optimization is performed for each model, and robust validation techniques are implemented to ensure the reliability and generalizability of our findings. The clustered approach is chosen to carry out a comprehensive and systematic explanation of the different factors that may influence the lifespan of women who have survived the cancer and the use of ML and DL models as a predictive tool. Survival anticipation for breast cancer is imperative in the context of personalized therapies. ML and DL models have proven to be powerful instruments in predicting the future states of patients based on their data.