Data driven healthcare insurance system using machine learning and blockchain technologies
Healthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot onl...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-07-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2980.pdf |
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| author | Irum Matloob Shoab Khan Bushra Bashir Rukaiya Rukaiya Javed Ali Khan Hessa Alfraihi |
| author_facet | Irum Matloob Shoab Khan Bushra Bashir Rukaiya Rukaiya Javed Ali Khan Hessa Alfraihi |
| author_sort | Irum Matloob |
| collection | DOAJ |
| description | Healthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot only identify and monitor doctors’ and hospitals’ behavior regarding the health services they provide to patients but can also recommend doctors and hospitals to insured employees based on the quality of services they provided previously. This system creates patient and doctor profiles separately, based on their rating. The proposed system combines singular value decomposition (SVD), K-nearest neighbors based collaborative filtering (KNN-based CF), item-based collaborative filtering (Item-based CF), content-based filtering using term frequency-inverse document frequency (TF-IDF), and K-means clustering and probability distributions to recommend doctors and insurance plans. The system measures similarity scores between patients and doctors using cosine similarity, which helps to determine similarity scores and refine the recommendations. This study also uses blockchain technology to automate insurance claims reimbursement. The results are validated using real data from the employees of a local hospital. The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. The insurance plans developed using the proposed system have reduced the overall expenditure of the local hospital, with a reduction in total expenses. Blockchain technology further helps prevent healthcare fraud. In the proposed system, a healthcare insurance claims reimbursement system is built using smart contract technology on the Ethereum blockchain, ensuring security & transparency and lowering the number of healthcare frauds. The system includes roles for the insurance company, healthcare provider, and patients. It also provides a platform for claim submission, approval, or refusal. In Pakistan, no such system existed before recommending doctors from different hospitals based on their professional conduct or the good health services they provide. |
| format | Article |
| id | doaj-art-3f5df549dc414cdfb6da81753edaed68 |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-3f5df549dc414cdfb6da81753edaed682025-08-20T02:47:43ZengPeerJ Inc.PeerJ Computer Science2376-59922025-07-0111e298010.7717/peerj-cs.2980Data driven healthcare insurance system using machine learning and blockchain technologiesIrum Matloob0Shoab Khan1Bushra Bashir2Rukaiya Rukaiya3Javed Ali Khan4Hessa Alfraihi5Department of Software Engineering, Fatima Jinnah Women University, Rawalpindi, PakistanNational University of Science and Technology, Islamabad, PakistanDepartment of Software Engineering, Fatima Jinnah Women University, Rawalpindi, PakistanSir Syed University of Engineering and Technology, Islamabad, PakistanDepartment of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire, United KingdomDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaHealthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot only identify and monitor doctors’ and hospitals’ behavior regarding the health services they provide to patients but can also recommend doctors and hospitals to insured employees based on the quality of services they provided previously. This system creates patient and doctor profiles separately, based on their rating. The proposed system combines singular value decomposition (SVD), K-nearest neighbors based collaborative filtering (KNN-based CF), item-based collaborative filtering (Item-based CF), content-based filtering using term frequency-inverse document frequency (TF-IDF), and K-means clustering and probability distributions to recommend doctors and insurance plans. The system measures similarity scores between patients and doctors using cosine similarity, which helps to determine similarity scores and refine the recommendations. This study also uses blockchain technology to automate insurance claims reimbursement. The results are validated using real data from the employees of a local hospital. The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. The insurance plans developed using the proposed system have reduced the overall expenditure of the local hospital, with a reduction in total expenses. Blockchain technology further helps prevent healthcare fraud. In the proposed system, a healthcare insurance claims reimbursement system is built using smart contract technology on the Ethereum blockchain, ensuring security & transparency and lowering the number of healthcare frauds. The system includes roles for the insurance company, healthcare provider, and patients. It also provides a platform for claim submission, approval, or refusal. In Pakistan, no such system existed before recommending doctors from different hospitals based on their professional conduct or the good health services they provide.https://peerj.com/articles/cs-2980.pdfHealth informaticsFraud detectionApplied machine learningBlockchainSecurity and privacyArtificial intelligence |
| spellingShingle | Irum Matloob Shoab Khan Bushra Bashir Rukaiya Rukaiya Javed Ali Khan Hessa Alfraihi Data driven healthcare insurance system using machine learning and blockchain technologies PeerJ Computer Science Health informatics Fraud detection Applied machine learning Blockchain Security and privacy Artificial intelligence |
| title | Data driven healthcare insurance system using machine learning and blockchain technologies |
| title_full | Data driven healthcare insurance system using machine learning and blockchain technologies |
| title_fullStr | Data driven healthcare insurance system using machine learning and blockchain technologies |
| title_full_unstemmed | Data driven healthcare insurance system using machine learning and blockchain technologies |
| title_short | Data driven healthcare insurance system using machine learning and blockchain technologies |
| title_sort | data driven healthcare insurance system using machine learning and blockchain technologies |
| topic | Health informatics Fraud detection Applied machine learning Blockchain Security and privacy Artificial intelligence |
| url | https://peerj.com/articles/cs-2980.pdf |
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