Machine Learning-Based Early Detection System for Medicare Complaints: A Predictive Framework for CMS Oversight

Authors: Anirudh Reddy Pathe

DOI: https://doi.org/10.5281/zenodo.14261098

Short DOI: https://doi.org/g8s4zs

Country: USA

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Abstract: This paper presents a comprehensive machine learning framework designed to detect and predict potential issues in Medicare services through early complaint pattern recognition. The proposed system leverages advanced natural language processing and supervised learning techniques to analyze incoming Medicare complaints, enabling the Centers for Medicare & Medicaid Services (CMS) to identify emerging problems before they escalate into systemic issues. By incorporating multiple machine learning algorithms and feature extraction methods, the framework achieves robust prediction capabilities while maintaining interpretability for healthcare administrators. The system's architecture is designed to process both structured and unstructured complaint data, providing actionable insights for proactive regulatory oversight

Keywords: Medicare complaints, machine learning, healthcare oversight, predictive analytics, natural language processing, supervised learning, regulatory compliance

Paper Id: 231714

Published On: 2018-02-07

Published In: Volume 6, Issue 1, January-February 2018

Cite This: Machine Learning-Based Early Detection System for Medicare Complaints: A Predictive Framework for CMS Oversight - Anirudh Reddy Pathe - IJIRMPS Volume 6, Issue 1, January-February 2018. DOI 10.5281/zenodo.14261098

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