[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120416-en":3,"doc-seo-120416-105":29,"detail-sidebar-cat-0-en-105":89},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120416,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Fraud Detection in Health Insurance Claims using Machine Learning - Optimized Classification Pipeline","Fraud Detection in Health Insurance Claims using Machine Learning presents a machine-learning approach to identify fraudulent healthcare insurance claims. Patient attributes, claim amounts, and medical details are preprocessed through missing-value handling, categorical encoding, and numerical standardization. Fraud is modeled as a classification task by labeling claims in the top 5% of the cost distribution. Logistic Regression, Random Forest, and XGBoost are trained and evaluated, with Random Forest delivering the best tuned performance. Visual analyses reveal fraud trends by age, region, smoking, and blood pressure, while feature selection, SMOTE balancing, and threshold tuning reduce overfitting and improve precision-recall balance for practical fraud prediction.","Abstract: Fraud Detection in Health Insurance Claims Using Machine Learning  \nThis project focuses on detecting fraudulent health insurance claims using machine learning techniques. The dataset includes various patient attributes, claim amounts, and medical details, which were preprocessed by handling missing values, encoding categorical features, and standardizing numerical data. Fraud detection was formulated asa classification problem, where claims in the top 5% of the cost distribution were labeled as potentially fraudulent. Several models, including Logistic Regression, Random Forest, and XGBoost, were trained and evaluated, with Random Forest providing the best performance after tuning.  \nTo gain deeper insights, multiple visualizations were created to analyze fraud patterns based on age, region, smoking habits, blood pressure levels, and feature correlations. While initial models exhibited overfitting, techniques such as feature selection, SMOTE balancing, and adjusting fraud detection thresholds improved generalization. The final optimized model achieved a balance between high precision and recall, making it suitable for real-world applications. Though deployment was initially considered, the project concluded with a locally usable model for fraud prediction, ensuring robust, data-driven decision-making for healthcare fraud detection.","cbCaioEaPHHzMX5N","https://ap.wps.com/l/cbCaioEaPHHzMX5N","pdf",25316,1,"English","en",105,"# Project Overview\n## Data Preprocessing and Labeling\n## Model Training and Evaluation\n## Feature Analysis and Visualizations\n## Overfitting Mitigation and Optimization\n## Final Model and Deployment Considerations","[{\"question\":\"How is fraud defined for the classification task?\",\"answer\":\"Fraudulent claims are labeled as those in the top 5% of the cost distribution, turning the problem into supervised classification.\"},{\"question\":\"Which models were trained and how is performance compared?\",\"answer\":\"Logistic Regression, Random Forest, and XGBoost are trained and evaluated; Random Forest provides the best performance after tuning.\"},{\"question\":\"What techniques improve generalization and reduce overfitting?\",\"answer\":\"Feature selection, SMOTE class balancing, and adjusting fraud detection thresholds help reduce overfitting and improve precision-recall performance.\"}]","Fraud Detection in Health Insurance Claims using Machine Learning - Optimized Classification Pipeline | PDF",1785729933,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"fraud-detection-in-health-insurance-claims-using-machine-learning-optimized-classification-pipeline","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/fraud-detection-in-health-insurance-claims-using-machine-learning-optimized-classification-pipeline/120416/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"How is fraud defined for the classification task?","Question",{"text":73,"@type":74},"Fraudulent claims are labeled as those in the top 5% of the cost distribution, turning the problem into supervised classification.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which models were trained and how is performance compared?",{"text":78,"@type":74},"Logistic Regression, Random Forest, and XGBoost are trained and evaluated; Random Forest provides the best performance after tuning.",{"name":80,"@type":71,"acceptedAnswer":81},"What techniques improve generalization and reduce overfitting?",{"text":82,"@type":74},"Feature selection, SMOTE class balancing, and adjusting fraud detection thresholds help reduce overfitting and improve precision-recall performance.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]