Enhancing Healthcare Fraud Detection: A Comparative Analysis of Machine Learning Models

The rapid development of Artificial Intelligence and Machine Learning has revolutionized the healthcare sector, particularly in predictive analytics, fraud detection, and diagnosis. AI-driven innovations have significantly improved medical decision-making systems, resulting in better patient outcomes while safeguarding data and combating fraud. This research now seeks to study and assess AI-based models maximizing safety and efficiency in the healthcare sector. It is primarily concerned with smart diagnosis, medical data security of blockchain, and anti-fraud support of AI. Through referencing various studies, the study emphasizes the ways machine learning algorithms like Random Forest, CNN, and ANN support disease diagnosis and curb medical transaction frauds. The paper gathers empirical evidence and case studies indicating AI's function within the healthcare sector. It analyzes machine learning-driven diagnosis tools and how accurate they are in disease prognosis, analyzes systems of fraud detection, and investigates the use of blockchain for secure processing of healthcare information. Inclusion of AI and ML in health care has created amazing innovations in medical data security, real-time fraud detection, and patient therapy. The research points to the necessity of using AI-driven models in modern-day healthcare systems with an impetus for further investigation of integrated AI-blockchain methodology and enhanced ML-based diagnostic methods.