Efficient Predictive interpretable analytics models for Claims Cost Management in Healthcare using CNN-Ant Colony Optimization
Keywords:
Healthcare claims cost management, Generative Adversarial Networks (GAN), Ant Colony Optimization (ACO)GAN-ACO Machine learning hybridmodel, Costprediction, allocation and Interpretable analytics modelsAbstract
Predicting and optimising for costs associated with healthcare claims is a very complicated topic that calls for advanced analytic approaches. In this work, we propose a new hybrid model of GAN and ACO to overcome the presented challenge. In the model, the GAN part is used to choose important features out of medical claims data and the results obtained from it are then provided to ACO part which helps minimize overall costs by allocating healthcare resources accordingly. We apply the proposed GAN-ACO hybrid model to real-world health care claims data and compare its performance against traditional machine learning and optimization methods. Experimental results show that compared to the benchmark methods, the GAN-ACO model has excellent prediction accuracy and resource allocation performance. Finally, our combined hybrid model as this results in the mean absolute error cost and root mean square error costs are 0.15 and respective of 0.22 for price prediction, contributing around 18% relative savings in overall resource allocation costs against baseline methods. The model also interpretability is also assessed out to see what leads to healthcare claims costs, such as age factor, medical history, along with treatment complexity. These results can inform healthcare administrators and policymakers about claims cost containment and allocation strategies. The suggested fit of GAN and ACO model, holds significant potential in making the process of healthcare claims cost management more efficient and effective as it comes to practicality.
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