Healthcare Fraud Detection Using an Integrated ML Approach with SMOTE

Healthcare fraud is a growing problem with significant financial ramifications for healthcare systems worldwide, demanding effective detection measures to preserve resources and assure the delivery of excellent treatment. This paper presents a novel approach to fraud detection that integrates Random Forest and K-Nearest Neighbors (KNN) algorithms with the Synthetic Minority Over-sampling Technique (SMOTE) to effectively address the challenges of identifying fraudulent healthcare claims. Our research tackles the common issue of class imbalance in training datasets, where legitimate transactions vastly outnumber fraudulent ones. By generating synthetic samples of fraudulent claims, SMOTE enhances the model’s learning capabilities, leading to improved detection accuracy. Our approach consisted of thorough data preparation, the creation of an integrated model, and a rigorous assessment using several performance measures including accuracy, precision, recall, and F1-score. The model exhibits exceptional performance in detecting fraud, achieving remarkable scores in accuracy 97.96%, precision 94.96%, F1-score 97.41%, and AUC 96.44%. This research ofers a promising solution to healthcare fraud by leveraging sophisticated methodologies, empowering healthcare providers and insurers to better safeguard their resources.