D I G I T A L D I A G N O S T I C S
Herat Joshi , Data Science Lead at Great River Health accuracy of 98.83 %, significantly better than any individual model ’ s performance .
Each model offers unique strengths : one excels at processing complex health data , another detects hidden patterns , while others reduce variability and improve differentiation between health conditions . Together , they form a robust tool for detecting breast cancer and advancing personalised care in women ’ s health .
Addressing the issue of class imbalance
Class imbalance , where malignant cases are outnumbered by benign ones , is a significant challenge in breast cancer diagnosis . By using advanced techniques like resampling and synthetic data generation ( such as SMOTE ), ensemble models can ensure that both malignant and benign cases are properly represented . This reduces the risk of missing a cancer diagnosis and improves the overall accuracy of early detection .
Impact in clinical settings
The potential impact of ensemble AI techniques in clinical settings is profound , particularly in reducing the need for invasive diagnostic procedures like biopsies . Although biopsies are still the gold standard for confirming cancer , they can be invasive , costly and cause discomfort for patients . AI-powered diagnostic tools , particularly
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