[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126211-en":3,"doc-seo-126211-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126211,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","HEALTHCARE FRAUD DETECTION USING MACHINE LEARNING ENSEMBLE METHODS - Abstract","Healthcare fraud detection addresses major financial losses and degraded patient care caused by false claims, duplicate billing, and identity theft. The work uses ensemble-based machine learning to improve robustness against evolving fraud patterns and large-scale medical claim data. It employs boosting algorithms such as AdaBoost, Gradient Boosting, and XGBoost, and integrates SMOTE to handle class imbalance. Experiments on a Kaggle Medicare claim dataset show improved classification accuracy, recall, and correctness, supporting proactive fraud identification. Future work suggests combining deep learning and descriptive AI techniques for better detection and explainability.","HEALTHCARE FRAUD DETECTION USING MACHINE LEARNING ENSEMBLE METHODS SEEJPH Volume XXVI, S1,2025, ISSN: 2197-5248; Posted:05-01-25  \nHEALTHCARE FRAUD DETECTION USING MACHINE LEARNING ENSEMBLE METHODS  \nRasanarayan Chaurasiya1, Kirti Jain1,*  \n1Research Scholar, Sanjeev Agrawal Global Educational University, Bhopal, India  \n1, *Associate Professor, CSE Dept., Sanjeev Agrawal Global Educational University, Bhopal, India  \n*Correspondence: [rnchaurasiya6@gmail.com](rnchaurasiya6@gmail.com)  \n\n| KEYWORDS | ABSTRACT |\n| --- | --- |\n| Healthcare | Healthcare fraud can lead to significant financial losses and disrupt patient care. |\n| fraud detection, | Detecting fraud in medical claims is a challenging task due to the volume of data |\n| machine | and changing fraud patterns. Machine learning (ML) techniques, especially |\n| learning, | boosting techniques, have shown great success in improving the accuracy of fraud |\n| Ensemble | detection. Boosting algorithms such as Adaptive Boosting (AdaBoost), Gradient |\n| learning, | Boosting, and Extreme Gradient Boosting (XGBoost) improve predictive |\n| Boosting, | performance by combining weak classifiers into a robust model. This paper has |\n| XGBoost, | develop a framework by using ensemble learning based ML models like XGBoost, |\n| LightGBM | LightGBM, and found that they performs well as compare to other methods. The method also uses SMOTE for resolving class imbalance problem in dataset. The work has been performed on Medicare claim dataset provided by Kaggle. This paper investigates the use of a special technology for medical fraud detection and compares its results with other models. Experimental results show that the developed system can improve the accuracy of classification, recall, and correctness, becoming a powerful tool for medical care fraud detection. Future research can focus on the integration of deep learning and descriptive AI techniques to improve fraud detection and further explanation. |\n\n1. Introduction  \nHealthcare fraud is a significant challenge that leads to substantial financial losses and affects the quality of care provided to patients. Fraudulent activities in healthcare include false claims, billing for unprovided services, duplicate billing, and identity theft (He et al., 2021). According to the National Health Care Anti-Fraud Association (NHCAA), healthcare fraud costs the industry billions of dollars annually, impacting insurers, healthcare providers, and patients (NHCAA, 2023).According to the Federal Bureau of Investigation (FBI), healthcare fraud costs the United States an estimated $100 billion annually, impacting healthcare costs and service quality (FBI, 2022).Fraudulent claims drive up healthcare costs and premiums, leading to increased expenses for patients and insurers (CMS, 2023).The Global Healthcare Fraud Analytics Market size is expected to be worth around USD 20.4 Billion by 2033 from USD 2.5 Billion in 2023, growing at a CAGR of 23.5% during the forecast period from 2024 to 2033 shown in figure below ([market.us 2023](market.us 2023)).  \nHEALTHCARE FRAUD DETECTION USING MACHINE LEARNING ENSEMBLE METHODS SEEJPH Volume XXVI, S1,2025, ISSN: 2197-5248; Posted:05-01-25  \nFigure 1: Global healthcare fraud analytic market ([source maket.us 2023](source maket.us 2023))  \nHealthcare fraud is a form of white-collar crime characterized by the fraudulent submission of healthcare claims for illicit financial gain, usually by organized crime groups and dishonest healthcare practitioners. Common tactics may include billing for costly services or procedures that were never covered by insurance plans; misrepresenting non-covered treatments; engaging in insurance scams and engaging in other illegal practices. Healthcare fraud analytics utilize fraud detection solutions and software designed to detect instances of healthcare fraud such as false claim submissions duplicated claims submissions; pharmacist prescription fraud and health insurance fraud etc.  \nIn toda","cbCaiivmSfQVZ5qj","https://ap.wps.com/l/cbCaiivmSfQVZ5qj","pdf",336716,1,7,"English","en",105,"# Introduction\n## Problem background and impact\n## Role of machine learning in fraud detection\n## Scope and motivation of the study","[{\"question\":\"What types of healthcare fraud are discussed in the document?\",\"answer\":\"The document describes false claims, billing for services not provided, duplicate billing, and identity theft, as common healthcare fraud tactics.\"},{\"question\":\"Why are ensemble machine learning methods used for fraud detection?\",\"answer\":\"Ensemble methods combine weak classifiers into a robust model, improving predictive performance and adapting better to changing fraud patterns than static rule-based approaches.\"},{\"question\":\"How does the method handle class imbalance in the dataset?\",\"answer\":\"The paper uses SMOTE to resolve the class imbalance problem, improving model learning on minority fraud cases.\"}]","HEALTHCARE FRAUD DETECTION USING MACHINE LEARNING ENSEMBLE METHODS - Abstract | PDF",1785903808,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"healthcare-fraud-detection-using-machine-learning-ensemble-methods-abstract","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/healthcare-fraud-detection-using-machine-learning-ensemble-methods-abstract/126211/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What types of healthcare fraud are discussed in the document?","Question",{"text":76,"@type":77},"The document describes false claims, billing for services not provided, duplicate billing, and identity theft, as common healthcare fraud tactics.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why are ensemble machine learning methods used for fraud detection?",{"text":81,"@type":77},"Ensemble methods combine weak classifiers into a robust model, improving predictive performance and adapting better to changing fraud patterns than static rule-based approaches.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the method handle class imbalance in the dataset?",{"text":85,"@type":77},"The paper uses SMOTE to resolve the class imbalance problem, improving model learning on minority fraud cases.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]