VitaShield AI: Achieving Near-Perfect Precision in Breast Cancer Diagnosis with Tuned LightGBM and SHAP Interpretability

Breast cancer remains a significant health issue across the world, and early and precise diagnosisis a key issue in enhancing patient outcomes. This paper introduces VitaShield AI, a highperformance machine learning pipeline that is used in binary tumor classification (benign andmalign) on the well-known Wisconsin Breast Cancer Diagnostic (WBCD) dataset with the helpof SMOTE oversampling to correct class balance and wide-ranging hyperparameter optimizationwith the help of GridSearchCV. The last model obtains the accuracy of 97.37, the precision of100, the recall of 92.86 and the F1-score of 96.30 on the independent test set and yields theROC-AUC of 99.24. Model interpretability is achieved by means of SHAP (SHapley AdditiveexPlanations) values, which indicate that concave points worst, perimeter worst and radius worstare the strongest malignancy predictors. These findings indicate that VitaShield AI represents aclinical utility with exceptional levels and, specifically, the zero false positives (100 percentaccuracy) of the model is of crucial importance in reducing the number of unnecessary invasiveoperations.