[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124204-en":3,"doc-seo-124204-105":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},124204,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana - A Data-Driven Machine Learning Approach","Nursing and Midwifery Informatics (NMI) develops healthcare professionals’ capability to use emerging technologies in clinical practice. This study evaluates NMI educational programs in Ghana through machine learning analysis of factors affecting student performance, engagement, and satisfaction. Data from 1,500 students across five institutions is modeled using Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression with standard metrics. Gradient Boosting reaches 95% predictive accuracy, highlighting engagement and curriculum satisfaction as key predictors. Institutional differences significantly shape academic outcomes, with students at Tamale Nursing and Midwifery Training College outperforming those at C.K. Tedam University of Technology and Applied Sciences (β = 3.85, p = 0.021).","Journal of Information Systems and Informatics  \nVol. 7, No. 1, March 2025 e-ISSN: 2656-4882 p-ISSN: 2656-5935  \nDOI: 10.51519/journalisi.v7i1.1018 Published By DRPM-UBD  \nRevolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine  \nLearning Approach  \nIven Aabaah1, Japheth Kodua Wiredu2,*, Bakaweri Emmanuel Batowise3, Nelson Seidu Abuba4  \n1Department of Information Systems & Technology, C. K. Tedam University of Technology & Applied Sciences, Navrongo, Ghana  \n2,4Department of Computer Science, Regentropfen University College, Upper East, Ghana.  \n3Department of Cyber Security and Computer Engineering Technology, C. K. Tedam University of Technology & Applied Sciences, Navrongo, Ghana [Email:](Email:1iaabaah@cktutas.edu.gh)[1](Email:1iaabaah@cktutas.edu.gh)[iaabaah@cktutas.edu.gh](Email:1iaabaah@cktutas.edu.gh), [2](2wiredujapheth130@gmail.com)[wiredujapheth130@gmail.com](2wiredujapheth130@gmail.com), [3](3 ebbakaweri@cktutas.edu.gh)[ ebbakaweri@cktutas.edu.gh](3 ebbakaweri@cktutas.edu.gh),  \n[4](4 nelson.seidu@regentropfen.edu.gh)[ nelson.seidu@regentropfen.edu.gh](4 nelson.seidu@regentropfen.edu.gh)  \nAbstract  \nThe field of Nursing and Midwifery Informatics (NMI) aims to equip healthcare professionals with the skills to efficiently use emerging technologies in their practice. This research assessed NMI educational programs in Ghana using machine learning techniques to analyze key factors influencing student performance, engagement, and satisfaction. Data was gathered from 1,500 students across C.K. Tedam University of Technology and Applied Sciences, Bolgatanga Nursing and Midwifery Training College, Regentropfen University College, Tamale Nursing and Midwifery Training College, and University for Development Studies. The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. The Gradient Boosting model achieved the highest predictive accuracy at 95%, identifying student engagement and curriculum satisfaction as the most influential predictors of academic success. Additionally, multiple regression analysis revealed that institutional differences significantly influenced academic outcomes, with students at Tamale Nursing and Midwifery Training College outperforming their counterparts at C.K. Tedam University of Technology and Applied Sciences (β = 3.85, p = 0.021), likely due to better alignment between their curriculum and instructional methods. These findings offer actionable insights for curriculum development and healthcare policy planning in resourceconstrained settings, advocating for the integration of machine learning tools into academic evaluations. The study presents a scalable predictive model that can be adapted to enhance digital health education in similar low-resource settings worldwide, offering a pathway to more effective and inclusive healthcare education systems.  \nKeywords: Nursing and Midwifery Informatics, Machine Learning in Education, Academic Performance Prediction, Digital Health Education, Curriculum and Student Engagement Analysis  \n442  \nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \np-ISSN: 2656-5935 [http://journal-isi.org/index.php/isi](http://journal-isi.org/index.php/isi) e-ISSN: 2656-4882  \n1. INTRODUCTION  \nThe rapid evolution of healthcare technology has necessitated significant advancements in educational curricula designed to prepare healthcare professionals[1], [2] .Nursing and Midwifery Informatics (NMI) has emerged as a critical component of healthcare education, integrating technological systems with clinical practices to enhance patient care standards, improve healthcare efficiency, and support data-driven medical decision-making[3], [4] . However, the effectiveness of NMI educational programs remains understudied, particularly in reso","cbCaifkDV33X9HOD","https://ap.wps.com/l/cbCaifkDV33X9HOD","pdf",657804,1,19,"English","en",105,"# Introduction\n## Nursing and Midwifery Informatics in Healthcare Education\n## Global Adoption and Regional Gaps\n## Informatics Competencies and Clinical Outcomes","[{\"question\":\"What is the purpose of the NMI curriculum evaluation in Ghana?\",\"answer\":\"The study assesses NMI educational programs across Ghanaian institutions to determine their impact on student competencies and identify improvement areas.\"},{\"question\":\"Which machine learning models were used to analyze student outcomes?\",\"answer\":\"Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression were used, evaluated with accuracy, precision, and recall.\"},{\"question\":\"What model performed best and which factors were most influential?\",\"answer\":\"Gradient Boosting achieved the highest predictive accuracy at 95%, with student engagement and curriculum satisfaction emerging as the most influential predictors of academic success.\"}]","Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana - A Data-Driven Machine Learning Approach | PDF",1785821003,48,{"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},"revolutionizing-nursing-and-midwifery-informatics-curriculum-evaluation-in-ghana-a-data-driven-machine-learning-approach","",{"@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/revolutionizing-nursing-and-midwifery-informatics-curriculum-evaluation-in-ghana-a-data-driven-machine-learning-approach/124204/",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","2026-08-04",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},"What is the purpose of the NMI curriculum evaluation in Ghana?","Question",{"text":75,"@type":76},"The study assesses NMI educational programs across Ghanaian institutions to determine their impact on student competencies and identify improvement areas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used to analyze student outcomes?",{"text":80,"@type":76},"Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression were used, evaluated with accuracy, precision, and recall.",{"name":82,"@type":73,"acceptedAnswer":83},"What model performed best and which factors were most influential?",{"text":84,"@type":76},"Gradient Boosting achieved the highest predictive accuracy at 95%, with student engagement and curriculum satisfaction emerging as the most influential predictors of academic success.","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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]