[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-504944-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-504944-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","the-journal-of-computer-science-and-its-applications-vol-25-june-2018","THE JOURNAL OF COMPUTER SCIENCE AND ITS APPLICATIONS - Vol 25 - June 2018","","Epidemic diseases cause severe harm globally, with HIV/AIDS remaining the most serious challenge in many developing regions, especially Sub-Saharan Africa. This paper develops a predictive model for pediatric HIV/AIDS survival in Nigeria using data from 216 pediatric patients receiving antiretroviral therapy. Virologists and pediatricians identify survival-relevant variables, including CD4 count, viral load, opportunistic infection, and nutrition status. A 10-fold cross-validation approach and a naïve Bayes classifier implemented in WEKA produce 81.02% accuracy, with an ROC AUC of 0.933, and validation against historical data from two tertiary institutions.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/the-journal-of-computer-science-and-its-applications-vol-25-june-2018/504944/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/the-journal-of-computer-science-and-its-applications-vol-25-june-2018/504944.png","ImageObject",300,407,{"name":42,"@type":43},"lilac","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-20","2026-10-04",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What goal does the paper address for pediatric HIV/AIDS patients in Nigeria?","Question",{"text":63,"@type":64},"The paper aims to predict pediatric HIV/AIDS survival in Nigeria by building a predictive model based on identified clinical and patient variables.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which machine learning method and variables are used to develop the survival model?",{"text":68,"@type":64},"A naïve Bayes classifier is used, with features including CD4 count, viral load, opportunistic infection, and nutrition status.",{"name":70,"@type":61,"acceptedAnswer":71},"How is the model evaluated and how accurate is it?",{"text":72,"@type":64},"The study uses 10-fold cross validation with training and testing splits, achieving 81.02% accuracy and an ROC AUC of 0.933 for discriminating survived versus non-survived cases.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},504944,1791352437,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},3985747861865,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","The Journal of Computer Science and its Applications Vol. 25, No 1, June, 2018  \nTHE JOURNAL OF COMPUTER SCIENCE AND ITS APPLICATIONS Vol. 25, No 1, June, 2018  \n\n| PREDICTION OF PEDIATRIC HIV/AIDS SURVIVAL IN NIGERIA USING NAÏVE BAYES’ APPROACH\u003Cbr>P.A. Idowu1, T. A. Aladekomo2, O. Agbelusi3, and C. O. Akanbi4\u003Cbr>1,2 Obafemi Awolowo University, Ile-Ife, Nigeria,\u003Cbr>3 Rufus Giwa Polytechnic, Owo, Nigeria\u003Cbr>4 Osun State University, Nigeria\u003Cbr>[1](1paidowu1@yahoo.com)[paidowu1@yahoo.com](1paidowu1@yahoo.com) , [4](4akanbico@uniosun.edu.ng)[akanbico@uniosun.edu.ng](4akanbico@uniosun.edu.ng) |\n| --- |\n| ABSTRACT\u003Cbr>Epidemic diseases have highly destructive effects around the world and these diseases have affected both developed and developing nations. Disease epidemics are common in developing nations especially in Sub Saharan Africa in which Human Immunodeficiency Virus /Acquired Immunodeficiency Disease Syndrome (HIV/AIDS) is the most serious of all. This paper presents a prediction of pediatric HIV/AIDS survival in Nigeria. Data are collected from 216 pediatric HIV/AIDS patients who were receiving antiretroviral drug treatment in Nigeria was used to develop a predictive model for HIV/AIDS survival based on identified variables. Interviews were conducted with the virologists and pediatricians to identify the variables predictive for HIV/AIDS survival in pediatric patients. 10-fold cross validation method was used in performing the stratification of the datasets collected into training and testing datasets following data preprocessing of the collected datasets. The model was formulated using the naïve Bayes’ classifier – a supervised machine learning algorithm based on Bayes’ theory of conditional probability and simulated on the Waikato Environment for Knowledge Analysis [WEKA] using the identified variables, namely: CD4 count, viral load, opportunistic infection and the nutrition status of the pediatric patients involved in the study. The results showed 81.02% accuracy in the performance of the naïve Bayes’ classifier used in developing the predictive model for HIV/AIDS survival in pediatric patients. In addition, the area under the receiver operating characteristics [ROC] curve had a value of 0.933 which showed how well the developed predictive model was able to discriminate between survived and non-survived cases. Model validation was performed by comparing the model results with that of historical data from two (2) selected tertiary institutions in Nigeria.\u003Cbr>Keywords: HIV/AIDS survival, naïve Bayes’ classifier, predictive model, pediatrics, machine learning |\n\n1.0 Introduction  \nEpidemic diseases have highly destructive effects around the world and these diseases have affected both developed and developing nations. Disease epidemics are common in developing nations especially in Sub Saharan Africa in which Human Immunodeficiency  \nVirus /Acquired Immunodeficiency Disease Syndrome (HIV/AIDS) is the most serious of all [1] . HIV is one of the world’s most serious health and development challenges [2] . It is a type of virus called a retrovirus which infects humans when it comes in contact with tissues such as those that line the vagina, anal area,  \nmouth, eyes or through a break in the skin [3], while Acquired Immunodeficiency Syndrome (AIDS) is the advanced stage of the retroviral infection that swept through sub-Saharan Africa with venom [4-5] .  \nGlobally, HIV continues to be a very serious health issue facing the world [6] . About 34 million (31.4 million–35.9 million) people were living with HIV at the end of 2011 and an estimated0.8% of adults aged 15- 49 years worldwide are living with the virus, although the burden of the epidemic continues to vary considerably between countries and regions [6] . In Sub-Saharan Africa, roughly 25 million people were living with HIV in 2012, accounting for nearly 70 percent of the global total. The epidemic has had widespread social and economic consequences, not only in the health s","cbCaighkSbTqYpBA","https://ap.wps.com/l/cbCaighkSbTqYpBA","pdf",16403523,173,"English","# ABSTRACT\n# 1.0 Introduction","[{\"question\":\"What goal does the paper address for pediatric HIV/AIDS patients in Nigeria?\",\"answer\":\"The paper aims to predict pediatric HIV/AIDS survival in Nigeria by building a predictive model based on identified clinical and patient variables.\"},{\"question\":\"Which machine learning method and variables are used to develop the survival model?\",\"answer\":\"A naïve Bayes classifier is used, with features including CD4 count, viral load, opportunistic infection, and nutrition status.\"},{\"question\":\"How is the model evaluated and how accurate is it?\",\"answer\":\"The study uses 10-fold cross validation with training and testing splits, achieving 81.02% accuracy and an ROC AUC of 0.933 for discriminating survived versus non-survived cases.\"}]","THE JOURNAL OF COMPUTER SCIENCE AND ITS APPLICATIONS - Vol 25 - June 2018 | PDF",1791080420,436]