Bladder cancer, when diagnosed at an advanced stage, often necessitates inevitable invasive intervention. Consequently, non-invasive biosensor-based cancer detection and AI-based precision screening are being actively employed. However, the misclassification of cancer patients as normal-referred to as false negatives-remains a significant concern, as it could lead to fatal outcomes in lifespan. Moreover, while ensemble techniques such as soft voting and other methods can improve model accuracy and reduce misclassification, their effectiveness is limited and not applicable to all diagnostic tasks. Here, we developed a double stage cancer screening system that utilizes a sensitive urinary electrical biosensor implemented with an AI model and XAI interpretation tools. This system is designed for screening bladder cancer, well-known for its notable recurrence and high tendency to advance from non-invasive muscle tumors to muscle-invasive tumors. Four urinary biomarkers (CK8, CK18, PD-1, PD-L1) were measured by a field-effect transistor biosensor, and along with gender and age information, patients underwent initial screening by the CatBoost classification model. Patients initially classified as normal were reclassified using local explanations from neural networks offering a different perspective than CatBoost. After the second-stage screening, all of the false negatives from the initial screening could be correctly reclassified as cancer patients. Furthermore, global explanation guides the improvement of the AI model to be trained on an appropriate set of biomarker features to achieve high accuracy.