OPTIMIZING HEALTHCARE INSURANCE PREDICTIONS THROUGH DEEP LEARNING AND SCO-ENHANCED HYPERPARAMETER TUNING

Abstract

The healthcare insurance industry struggles to address three major issues which include processing large data volumes and detecting fraudulent activities as well as risk and claims prediction. Current approaches generally fail with intricate operations because they do not process high-dimension data along with new market directions. This paper demonstrates the use of deep learning models for healthcare insurance while demonstrating how Stochastic Coordinate Optimization (SCO) improves their operational efficiency for hyperparameter optimization. Deep learning demonstrates value in vast data analysis through automatic pattern discovery which brings powerful effects to identify fraud and forecast risks and handle claims. The paper reveals SCO stands ahead of classic hyperparameter optimization approaches grid search and random search because it swiftly optimizes performance in extensive search areas. When compared to the machine learning models SVM, Random Forest and KNN and CNN the proposed approach achieves better performance through enhanced accuracy along with improved sensitivity along with specificity and precision and F1-score. Operational use of SCO elevates the proposed deep learning model's effectiveness to establish it as a highly effective tool for healthcare insurance companies. Deep learning models optimized through SCO show promise to transform healthcare insurance operations by delivering better services with reduced costs and customized insurance packages according to their conclusion.