Region-based Convolutional Neural Network (RCNN) Model for Detecting Healthcare Fraud Spam

Ensuring the safety of patient care and the authenticity of the healthcare delivery system relies on rooting out fraudulent practices. Using the Region-based Convolutional Neural Network (RCNN) deep learning (DL) model, this research offered a unique technique to the identification of fraudulent activity in the healthcare industry. The RCNN model is trained on a dataset that contains spam data linked to healthcare. This dataset includes a variety of fraudulent acts, including identity theft and fake claims, along with other types of fraudulent activity. Using painstaking preprocessing, feature engineering, and model tuning, the RCNN architecture displays better performance in reliably identifying and localizing fraudulent patterns within healthcare data. Within the framework of the healthcare sector, this is achieved. Evaluation criteria like as accuracy, precision, recall, and F1-score are used to verify the effectiveness of the suggested strategy in comparison to conventional methods of fraud detection. The RCNN technique has demonstrated a performance improvement of 12.192 %, 13.475 %, 5.775 %, and 4.705 % when compared to existing approaches such as SVM, DT, CNN, and LSTM. In addition, ethical problems concerning patient privacy and the interpretability of models are discussed, with an emphasis placed on the significance of responsible deployment in healthcare systems.