Research and AI projects involving dataset preprocessing/validation and explainable hybrid ensemble learning for breast cancer diagnosis.
DiagnosisThe candidate performed research-based AI training workflows using a breast cancer dataset, including dataset preprocessing and cleaning steps to prepare data for model training and evaluation. The work included data annotation and validation as part of building reliable supervised learning inputs, followed by model evaluation using diagnostic performance metrics. Explainable AI techniques (SHAP and LIME) were used to analyze and report model behavior relevant to clinical decision-support objectives. • Dataset preprocessing and cleaning with PCA and SMOTE. • Data annotation/validation as part of supervised learning pipeline readiness. • Model evaluation using confusion matrices, ROC-AUC, precision, recall, and F1-score. • Explainability and quality analysis using SHAP and LIME for reporting.