A light gradient boosting machine learning-based approach for predicting clinical data breast cancer

Breast Cancer Prediction (BCP) is a pivotal aspect of healthcare, and this research paper delves into exploring diverse methodologies outlined in the literature. Recent trends spotlight an increasing reliance on machine learning (ML) methods for predictive analysis of breast cancer within medical records. Despite the plethora of studies in this domain, a critical need persists for further investigation to pinpoint the most accurate approach. This work has identified a novel solution by introducing LightGBM, one of the most powerful and efficient ML techniques used in BCP for clinical datasets. The developed approach involves careful model creation supported by comprehensive training, validation, and testing procedures to reach maximum efficiency. Experimental results and performance evaluations performed using common metrics justify the effectiveness of the proposed approach. In this work, the proposed LightGBM-based approach has shown remarkable accuracy through extensive experiments; it also solidifies its position through comprehensive comparisons with alternative performance results.