A Comparative Study of Using Various Machine Learning and Deep LearningBased Fraud Detection Models For Universal Health Coverage Schemes

A Comparative Study of Using Various Machine Learning and Deep Learning-Based Fraud Detection Models For Universal Health Coverage Schemes

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : Rohan Yashraj Gupta, Satya Sai Mudigonda, Pallav Kumar Baruah
DOI :  10.14445/22315381/IJETT-V69I3P216

Citation 

MLA Style: Rohan Yashraj Gupta, Satya Sai Mudigonda, Pallav Kumar Baruah  "A Comparative Study of Using Various Machine Learning and Deep Learning-Based Fraud Detection Models For Universal Health Coverage Schemes" International Journal of Engineering Trends and Technology 69.3(2021):96-102. 

APA Style:Rohan Yashraj Gupta, Satya Sai Mudigonda, Pallav Kumar Baruah. A Comparative Study of Using Various Machine Learning and Deep Learning-Based Fraud Detection Models For Universal Health Coverage Schemes  International Journal of Engineering Trends and Technology, 69(3),96-102.

Abstract
Fraud detection is an important area of research in the healthcare systems due to its financial consequences arising mainly from investigation costs, revenue losses, and reputational risk. To mitigate this, most of the companies adopt Machine Learning and/or Deep Learning-based fraud detection models. Efficient fraud detection models improve the performance of healthcare systems. Key challenges in building an efficient fraud detection model include

  • Data imbalance: skewed number of lesser fraudulent cases in comparison to the non-fraudulent cases,
  • Selection of classification model: use of appropriate machine learning or deep learning models to identify fraud or non-fraud cases

In this work, we have used three different data-imbalance techniques and six classification models to meet these challenges; we have also used six variants of neural network models. For this, we have used data from part of the world’s largest universal health coverage scheme called Ayushman Bharat (PM-JAY India). There were a total of 26 models that were tested as part of this study. The performance of these models was measured using various metrics such as accuracy, sensitivity, specificity, and F1-score. It was identified that a neural network model trained on undersampled data performed better than other models in this study. Code is available in the following link: https://github.com/RohanYashraj/Healthcare-Fraud-Detection

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Keywords
Ayushman Bharat, PM-JAY India, Largest universal health coverage scheme, Machine learning, Deep learning, Data imbalance, Actuarial techniques, Data embedding, Classification models.