[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126210-en":3,"doc-seo-126210-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126210,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Utilizing Machine Learning for Financial Management in Healthcare - Research Paper","Healthcare financial management enables cost efficiency, fraud prevention, and optimal resource allocation. This research examines how machine learning algorithms support financial decision-making in healthcare, focusing on risk assessment, fraud detection, cost prediction, and insurance claims management. Four ML models are evaluated using real financial data: Linear Regression, Random Forest, KMeans clustering, and Support Vector Machines. Results highlight high accuracy for cost prediction and fraud detection, improved risk analysis, and better insurance claim approval efficiency, while emphasizing ethical ML through privacy and regulatory considerations.","Utilizing Machine Learning for Financial Management in Healthcare SEEJPH Volume XXVI, S2,2025, ISSN: 2197-5248; Posted:03-02-25  \nUtilizing Machine Learning for Financial Management in  \nHealthcare  \nDr Islam LEBCIR1,Dr. Ashish Kumar Tamrakar2,Dr chitkala venkareddy3, Shabarisha N4,Anu Mehra5,Dr P Kiran Kumar Reddy6  \n1Lecturer Class B, Laboratory of Managerial Innovation, Governance and Entrepreneurship (LIMGE),Management Sciences,National Higher,School of Management (ENSM),Kolea, TIPAZA,ALGERIA,EMAIL:i.lebcir[ens@ensmanagement.edu.dz](ens@ensmanagement.edu.dz)  \n2Associate Professor, Computer Science & Engineering,RSR, Rungta College of Engineering &  \nTechnology,Durg,Durg, Chhattisgarh,Mail id: [ashish.tamrakar1987@gmail.com](ashish.tamrakar1987@gmail.com)  \n3Assistant professor, Social work, Central University of Karnataka, kalaburagi Kalaburagi, Karnataka,[Email Id-komalika19@gmail.com](Email Id-komalika19@gmail.com)  \n4Assistant Professor, School of Business and Management, Christ University, Bangalore  \nBangalore, Karnataka,Mail I’d: [shabarisha.narayan@gmail.com](shabarisha.narayan@gmail.com)  \n5Professor, Amity School of Engineering and Technology, Amity University, Noida Uttar Pradesh,Mail id: [amehra@amity.edu](amehra@amity.edu)  \n6Professor, CSE-AIML, MLR Institute of technology, Medchal, Hyderabad, Telangana  \nMail [id:kiran.penubaka@gmail.com](id:kiran.penubaka@gmail.com)  \nKEYWORDS  \nMachine Learning, Healthcare Finance, Cost Prediction, Fraud Detection, Insurance Claim Optimization.  \nABSTRACT  \nIn healthcare, financial management is an effective tool that ensures that the cost of healthcare is efficient, fraud free and resource allocation is optimally made. In this research we discuss the usage of machine learning (ML) algorithms in financial decision making in healthcare, especially with regard to risk assessment, fraud detection, cost prediction, and claims management in insurance. Real world financial data is used to implement and evaluate four ML models: Linear Regression, Random Forest, KMeans Clustering and Support Vector Machines (SVM) . The results show that in healthcare cost prediction, Linear Regression’s technique was able to achieve 89.6% accuracy in cost prediction and so very likely to be accurate enough for precise expenditure forecasting if the distribution of the cost remains very similar to this. Traditional rule-based systems were outperformed by Random Forest in detecting fraudulent claims with a 94.3% accuracy. Financial transactions were successfully grouped by K-Means Clustering with a silhouette score of 0.78 as well as improving risk analysis, while SVM performed well with an accuracy of 87.5% improving the process of approving insurance claims and reducing delays. The proposed approach significantly improves accuracy than existing financial models in terms of prediction, minimizes financial risks, and is more efficient in operation. Further by pursuing the study of data privacy issues and regulatory challenges, this work takes care of ethical ML implementation. To enhance financial security of healthcare further, future work should be carried onscalability, federated learning, and AI driven financial automation.  \nUtilizing Machine Learning for Financial Management in Healthcare SEEJPH Volume XXVI, S2,2025, ISSN: 2197-5248; Posted:03-02-25  \nI. INTRODUCTION  \nThe present costs of healthcare are becoming a major challenge for the healthcare providers, policymakers, and patients across the globe. Sustainable, cost effective and improved patient outcome depends on efficient financial management in healthcare. Manual data processing and rule based systems used in traditional financial management approaches are not accurate and not adaptable. Recently big strides in healthcare commodities and solutions have been brought to bear by machine learning (ML) . Large volumes of financial data are analyzed by ML algorithms, and future expenditures, aided by resource allocation and fraud detection","cbCaijf4g02gZDHz","https://ap.wps.com/l/cbCaijf4g02gZDHz","pdf",346256,9,1,14,"English","en",105,"# I. Introduction\n# II. Related Works","[{\"question\":\"How does machine learning improve financial management in healthcare?\",\"answer\":\"Machine learning enables data-driven decisions by identifying patterns, trends, and anomalies that traditional rule-based systems may miss, supporting predictive analytics for spending and revenue cycle processes.\"},{\"question\":\"Which machine learning models are evaluated for healthcare financial tasks?\",\"answer\":\"The study evaluates Linear Regression, Random Forest, KMeans clustering, and Support Vector Machines (SVM) to address cost prediction, fraud detection, risk analysis, and insurance claims handling.\"},{\"question\":\"What evidence of model performance is reported in the research?\",\"answer\":\"The results report 89.6% accuracy for cost prediction using Linear Regression, 94.3% accuracy for fraudulent claim detection using Random Forest, KMeans grouping with a silhouette score of 0.78, and SVM with 87.5% accuracy for improving insurance claim approval and reducing delays.\"}]","Utilizing Machine Learning for Financial Management in Healthcare - 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