[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120152-en":3,"doc-seo-120152-105":30,"detail-sidebar-cat-0-en-105":91},{"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":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},120152,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning for Health Insurance Prediction in Nigeria - AJERD Volume 7, Issue 2","Health insurance coverage remains critical for healthcare accessibility in Nigeria, where medical costs often fall on households. This paper predicts individuals’ likelihood of medical insurance coverage using four machine learning classifiers—Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine—trained on demographic, socioeconomic, and health-related variables. Models are evaluated with Accuracy, Precision, Sensitivity, F score, and AUC/ROC, complemented by feature-importance analysis to identify key determinants. Results inform targeted strategies to improve affordability and access for Nigerians.","ABUAD Journal of Engineering Research and Development (AJERD) ISSN (online): 2645-2685; ISSN (print): 2756-6811  \nVolume 7, Issue 2, 541-554  \n| Machine Learning for Health Insurance Prediction in Nigeria\u003Cbr>Oluwasogo Adekunle OKUNADE 1, Victor Enemona OCHIGBO2, Emmanuel Gbenga DADA3, Olayemi Mikail\u003Cbr>OLANIYI 1, Oluwatoyosi Victoria OYEWANDE1\u003Cbr>1Department of Computer Science, Faculty of Sciences, National Open University of Nigeria, Abuja, Nigeria\u003Cbr>[aokunade@noun.edu.ng](aokunade@noun.edu.ng/omolaniyi@noun.edu.ng/ofayemi@noun.edu.ng)[/](aokunade@noun.edu.ng/omolaniyi@noun.edu.ng/ofayemi@noun.edu.ng)[omolaniyi@noun.edu.ng](aokunade@noun.edu.ng/omolaniyi@noun.edu.ng/ofayemi@noun.edu.ng)[/](aokunade@noun.edu.ng/omolaniyi@noun.edu.ng/ofayemi@noun.edu.ng)[ofayemi@noun.edu.ng](aokunade@noun.edu.ng/omolaniyi@noun.edu.ng/ofayemi@noun.edu.ng)\u003Cbr>2Department of Knowledge Management and Communication, Agricultural Research Council of Nigeria, Abuja, Nigeria\u003Cbr>[v.ochigbo@arcn.gov.ng](v.ochigbo@arcn.gov.ng)\u003Cbr>3Department of Computer Science, Faculty of Physical Sciences, University of Maiduguri, Maiduguri, Nigeria\u003Cbr>[gbengadada@unimaid.edu.ng](gbengadada@unimaid.edu.ng) |\n| --- |\n| Corresponding Author: [v.ochigbo@arcn.gov.ng](v.ochigbo@arcn.gov.ng), +2348063888982\u003Cbr>Date Submitted: 07/06/2024\u003Cbr>Date Accepted: 26/11/2024\u003Cbr>Date Published: 26/12/2024 |\n\nAbstract: Health insurance coverage remains critical to healthcare accessibility, particularly in developing nations like Nigeria. This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers. The dataset utilized for analysis comprises demographic information, socioeconomic factors, and health-related variables collected from a diverse sample across Nigeria. Four models are trained and evaluated: Logistic Regression widely accepted for its simplicity and interpretability. Random Forest is a robust ensemble learning algorithm capable of capturing complex relationships within the data. The decision Tree model is simple to understand and visualize and the Support Vector Machine model is known for producing a very good classification. Furthermore, the performance metrics utilized to rate the predictive capabilities of the models are Accuracy, Precision, Sensitivity, F Score, and area under the Receiver Operating Characteristic (AUC & ROC Curve). Additionally, a features importance analysis is conducted for the identification of the dominant factors contributing to the prediction of the spread of medical insurance in Nigeria. The outcome of this paper gives insights in the efficiency of each machine learning models used to forecast medical insurance coverage, and identifying key determinants influencing insurance coverage can assist policymakers and healthcare stakeholders in devising targeted strategies to improve healthcare access and affordability for the Nigerian people.  \nKeywords: Ensemble Technique, Odd Ratio, Confusion Matrix, Feature Importance, Medical Insurance.  \n1. INTRODUCTION  \nHealth insurance is crucial to the healthcare system, providing individuals as well as families with financial security against high medical costs. This assent involves a person or a group and an insurance company in which the company undertakes part-payment or all of the medical expenses in exchange for suitable premiums. The Nigerian National Health Insurance Scheme (NHIS) is a government program designed to improve access to quality healthcare services for Nigerian citizens and persons of other nationalities who legally live in Nigeria.  \nThe National Health Insurance Scheme (NHIS) is an organization enacted by Degree 35, of 1999 (now Act 35) . It started operations officially in 2005 and operates as Public Private Partnership, providing convenient, affordable, and quality healthcare to all Nigerians by ","cbCaicJR6ObqKIOX","https://ap.wps.com/l/cbCaicJR6ObqKIOX","pdf",933160,1,14,"English","en",105,"# Abstract\n# Introduction\n## Health insurance and financial protection\n## NHIS/NHIA background and universal health coverage\n## Out-of-pocket spending and risk pooling policies","[{\"question\":\"Which machine learning techniques are used to predict health insurance coverage in Nigeria?\",\"answer\":\"The study uses Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers.\"},{\"question\":\"What data types are included in the dataset for model training?\",\"answer\":\"The dataset includes demographic information, socioeconomic factors, and health-related variables collected across Nigeria.\"},{\"question\":\"How are the models evaluated, and what performance measures are reported?\",\"answer\":\"Evaluation uses Accuracy, Precision, Sensitivity, F score, and AUC/ROC metrics to assess predictive capability.\"}]","Machine Learning for Health Insurance Prediction in Nigeria - AJERD Volume 7, Issue 2 | PDF",1785728459,35,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-health-insurance-prediction-in-nigeria-ajerd-volume-7-issue-2","",{"@graph":36,"@context":85},[37,54,68],{"@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/machine-learning-for-health-insurance-prediction-in-nigeria-ajerd-volume-7-issue-2/120152/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning techniques are used to predict health insurance coverage in Nigeria?","Question",{"text":75,"@type":76},"The study uses Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data types are included in the dataset for model training?",{"text":80,"@type":76},"The dataset includes demographic information, socioeconomic factors, and health-related variables collected across Nigeria.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated, and what performance measures are reported?",{"text":84,"@type":76},"Evaluation uses Accuracy, Precision, Sensitivity, F score, and AUC/ROC metrics to assess predictive capability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"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":106,"slug":138},19,"General","general"]