Early Detection for Cervical Cancer Using XG Boost Algorithm through ML
Keywords:
XGBoost, Cervical Cancer Detection, Machine Learning, Healthcare, Predictive ModelingAbstract
This scholarly investigation presented an innovative web-based framework specifically engineered for cervical cancer detection utilizing the XG Boost algorithm, a sophisticated machine learning methodology. Through strategic utilization of a multifaceted dataset encompassing demographic and clinical history parameters including age demographics, sexual behavior patterns, contraceptive utilization metrics, and diagnostic medical records, the developed framework incorporated XGBoost technology to substantially elevate diagnostic precision and dependability. The technological platform was strategically designed to optimize early detection protocols and intervention mechanisms, which were identified as fundamental factors for enhancing patient clinical outcomes within cervical cancer management paradigms. Through methodical assessment and comprehensive analytical procedures, the research effectively demonstrated XGBoost's exceptional capabilities in predictive modeling applications for cervical cancer identification, representing a substantial advancement in the application of machine learning technologies for healthcare outcome improvement.
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