Healthcare Fraudulence: Leveraging Advanced Artificial Intelligence Techniques for Detection
International Research Journal of Modernization in Engineering Technology and Science · 2024
This study investigates the effectiveness of advanced artificial intelligence (AI) techniques in healthcare fraud detection using claim data.Machine learning models, including Logistic Regression, Decision Tree, and Random Forest, are trained, validated, and tested on a comprehensive dataset encompassing Medicare claims.Performance metrics, including accuracy, F1-score are used to evaluate model effectiveness.Feature engineering, including feature selection using a correlation matrix, played a pivotal role in enhancing model accuracy and mitigating overfitting.Comparison with traditional fraud detection methods revealed the superiority of AI models, highlighting their adaptability and ability to capture complex fraud patterns.However, it is important to acknowledge the potential for false positives and false negatives, necessitating ongoing model monitoring and adaptation in dynamic healthcare fraud scenarios.This research underscores the potential of advanced AI techniques in revolutionizing healthcare fraud detection, offering a more accurate and scalable solution while acknowledging the need for continuous refinement in this critical domain.