• Pooja Kushwaha
• Prof. Hitesh Gupta
• Dr. Vivek Richhariya

challenge in healthcare, leading to significant financial losses and compromised service quality. This research focuses on optimizing the accuracy of fraud detection systems using a Long Short-Term Memory (LSTM) neural network, a proven technique for processing sequential and time-series data. The study investigates advanced techniques to enhance LSTM's performance, including hyperparameter tuning, data preprocessing, and feature engineering tailored for healthcare datasets. Additionally, the model integrates domain-specific insights to improve interpretability and scalability in real-world applications. Experimental results demonstrate improved accuracy, precision, and recall compared to traditional machine learning models. This work contributes to the development of efficient and reliable Medicare fraud detection systems, paving the way for better healthcare resource management and fraud prevention strategies.

Fraud Detection, Deep Learning, Healthcare, Long Term Short Memory (LSTM)

"Optimization Accuracy of Medicare Fraud detection in Healthcare using LSTM Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.a823-a830, December-2024, Available :http://www.jetir.org/papers/JETIR2412073.pdf

"Optimization Accuracy of Medicare Fraud detection in Healthcare using LSTM Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppa823-a830, December-2024, Available at : http://www.jetir.org/papers/JETIR2412073.pdf