Predictive Breast Cancer Modeling Using Machine Learning

Abstract

Breast Cancer Is A Form Of Tumor That Develops Within Breast Tissues And Remains The Most Prevalent Cancer Among Women Globally, Ranking As One Of The Leading Causes Of Female Mortality. This Survey Examines The Evolving Landscape Of Predictive Modeling For Breast Cancer Using Data Mining Techniques. It Investigates The Application Of Various Algorithms—including Decision Trees, Support Vector Machines, And Neural Networks—for Effective Breast Cancer Prediction. The Survey Provides A Thorough Review Of Processes Such As Feature Selection, Model Training, And Validation Strategies, And Synthesizes Key Findings From Multiple Datasets. By Critically Analyzing Existing Literature, This Work Aims To Deepen Understanding In The Field, Offering Insights Into The Advancements, Current Challenges, And Future Prospects Of Predictive Modeling In Breast Cancer Research. Ultimately, This Survey Contributes To The Broader Discourse On Data-driven Approaches In Healthcare, Emphasizing Their Role In Enhancing Diagnostic And Prognostic Capabilities In Breast Cancer Management.

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