Experimental Analysis Of Machine Learning Models In Breast Cancer Prediction Using Lifestyle Factors

Abstract

Machine learning models have a substantial track record in predicting breast cancer for early-stage diagnosis. However, a comparison of machine learning models based on lifestyle factors to determine the most effective approach under various degrees of accuracy remains scarce. This paper examines the effectiveness of machine learning models and approaches in predicting breast cancer based on lifestyle factors. This study employs a range of machine learning techniques, including employing six different machine learning models: Random Forest, Logistic Regression, Neural Networks, XGBoost, Support Vector Machines, and K-Nearest Neighbors to assess their predictive accuracy in early-stage cancer detection, focusing on lifestyle factors. The paper focuses on how machine learning models interpret lifestyle-related data, a less explored yet crucial aspect in breast cancer prediction. By experimentally comparing these models, the study aims to determine the specific contexts and conditions under which each model optimally functions. This experimental analysis is pivotal for advancing personalized medicine, guiding clinical decision-making, and shaping future interventions in breast cancer prevention and public health policy. Ultimately, this paper contributes to a deeper understanding of the intricate relationship between lifestyle factors and breast cancer risk, highlighting the potential of machine learning in transforming early cancer detection.