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Design and Study of Machine Learning Model based on Bagging for Breast Cancer Diagnosis

Abstract:

The rising incidence of breast cancer underscores the critical importance of early detection in enabling timely interventions to reduce serious health risks. Statistical analysis reveals that using specific attributes and bagging methods significantly enhances predictive accuracy, offering a strategic advantage in improving treatment outcomes. This improvement is particularly evident when comparing the use of a linear discriminant model to its application within a bagging framework. Results validated through the 5x2 statistical test demonstrate significant differences, supporting the hypothesis that the bagging technique markedly boosts performance levels.