Performance Analysis of Real-Time Data Processing for Health Insurance
Implementation plan:
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Step 1: Initially, we load and collect data from “Enhanced Health Insurance Claims Dataset”.
step 2: Then we preprocess the data using distributed stream processing systems.
Step 3: Then, we generate synthetic data samples, enhancing model robustness against overfitting and underfitting using Generative Adversarial Networks (GANs).
Step 4: Next, we implement K-Means SMOTE (Synthetic Minority Over-sampling Technique) to handle data imbalance issues by generating targeted synthetic samples.
Step 5: Next, we analyze collusion and detect fraudulent behavior using Nash Equilibrium-based Payoff Matrix for Evolutionary Game(NE-PMEG).
step 6: Next , we implement Adaptive Drift-Aware Neural Optimization (ADANO) to maintain accuracy in dynamic environment using Random Forests and Recurrent Neural Networks (RNN).
Step 7:Finally, we plot performance for the following metrics:
7.1: Number of epochs vs Accuracy(%)
7.2: Number of epochs vs Precision(%)
7.3: Number of epochs vs Recall(%)
7.4: Number of epochs vs F1-Score(%)
7.5: True Positive Rate vs False Positive Rate(%)
7.6: Number of Estimators Vs Classification Rate(%)
Software Requirement:
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1. Development Tool: Python – 3.11.4 or above
2. Operating System: Windows 10 or above (64-bit)
Dataset Link :
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Link : https://www.kaggle.com/datasets/leandrenash/enhanced-health-insurance-claims-dataset/data
Note:
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the above plan satisfies your requirement please confirm with us.
We perform the EXISTING Approach based on the Reference 1:Title: Healthcare insurance fraud detection using data mining.
