Prediction of Health Insurance Course Incorporating the Random Forest Algorithm

Abstract

The project focuses on claims processing, where the accuracy and timeliness of determining claim validity play a crucial role. By training the Random Forest algorithm on a comprehensive dataset consisting of patient records, medical procedures, billing codes, and claim outcomes, the system will learn complex patterns and relationships to predict the likelihood of a claim being valid or potentially fraudulent. This predictive capability will enable insurance companies to expedite the processing of legitimate claims while flagging suspicious ones for further investigation. Fraud detection is another critical aspect of health insurance operations. The project aims to utilize the ensemble learning properties of Random Forest to identify patterns indicative of fraudulent activities. By analyzing features such as billing patterns, provider behavior, and historical fraud cases, the system will build a robust model that can accurately detect and prevent fraudulent claims. This proactive approach will help insurance companies minimize financial losses and protect policyholders from the adverse effects of fraud.

Introduction

Machine Learning (ML), a subset of Artificial Intelligence (AI), enables computers to learn from data and make predictions without explicit programming. Key components of ML include data, algorithms, training, and evaluation, with algorithms broadly classified into supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled data for classification and regression tasks, unsupervised learning discovers patterns in unlabeled data via clustering and association, and reinforcement learning optimizes behavior through feedback.

A literature survey in ML involves defining the research topic, retrieving and evaluating relevant papers, summarizing methodologies and findings, identifying trends and gaps, and documenting insights.

For health insurance prediction, a step-by-step methodology using Random Forest includes: data collection, preprocessing, feature selection, training/testing split, model training, evaluation, hyperparameter tuning, feature importance analysis, deployment, and continuous monitoring. This structured approach ensures accurate, interpretable, and maintainable ML models for real-world applications.

Conclusion

In conclusion, the Random Forest algorithm offers a powerful and versatile tool for health insurance prediction, delivering accurate and interpretable results. Its robustness, feature importance analysis, and ability to handle complex datasets make it a valuable asset in the field of health insurance analytics and decision-making. Future research could focus on further improving the model\'s performance, exploring ensemble techniques, or integrating additional data sources to enhance prediction accuracy and address specific challenges in the health insurance domain.

References

[1] Raghavan P., El Gayar N. “Fraud detection using machine learning and deep learning”, 2019 international conference on computational intelligence and knowledge economy (ICCIKE), IEEE (2019), pp. 334-339. [2] Awoyemi J.O., Adetunmbi A.O., Oluwadare S.A., “Credit card fraud detection using machine learning techniques: A comparative analysis”, 2017 international conference on computing networking and informatics (ICCNI), IEEE (2017), pp. 1-9. [3] Breiman L.,”Random forests Machine Learning”, 45 (1) (2001), pp. 5-32. [4] Eshghi A., Kargari M. “Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty Expert Systems with Applications”, 121 (2019), pp. 382-392. [5] Eweoya I., Adebiyi A., Azeta A., Azeta A.E. “Fraud prediction in bank loan administration using decision tree”, Journal of Physics: Conference Series, 1299 (1) (2019).

Copyright

Copyright © 2025 T Venkateswarlu, D Rammohan Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.