[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123426-en":3,"doc-seo-123426-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},123426,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","The Potential of Machine Learning Techniques in Predicting Malaria Outbreaks in Nigeria - Article","Malaria is a mosquito-borne infectious disease caused by Plasmodium parasites and remains a major global health threat, driving substantial morbidity and mortality worldwide. Focusing on Nigeria, this study addresses the limited use of machine learning for malaria outbreak prediction by applying five supervised models—Naive Bayes, Support Vector Machines, Linear Regression, Logistic Regression, and K-Nearest Neighbor. Meteorological and incidence data from 2010 to 2020 are analyzed using Python (Scikit-learn/Anaconda). Naive Bayes delivers the strongest performance with about 79.1% average accuracy on both training and testing, while SVM follows near 75.45%, K-NN at 70.8%, and Logistic Regression at 68%; Linear Regression performs poorly at 26.05%.","CARITAS UNIVERSITY AMORJI-NIKE, EMENE, ENUGU STATE  \nCaritas Journal of Physical and Life Sciences  \nCJPLS, Volume 3, Issue 1 (2024)  \nArticle History: Received: 12th March, 2024 Revised: 17th april, 2024 Accepted: 7th May, 2024  \n1  \nThe Potential of Machine Learning Techniques in Predicting Malaria  \nOutbreaks in Nigeria  \n1Okoronkwo, M.C.,  \nOmankwu, Obinnaya.C.B.,  \nKanu, Chigbundu  \nDepartment of Computer Science, Michael Okpara University of Agriculture, Umudike  \n[saintbeloved@yahoo.com](saintbeloved@yahoo.com)  \nAbstract  \nMalaria, an infectious disease transmitted by mosquitoes and caused by protists of the Plasmodium genus, poses a significant global health threat, contributing substantially to morbidity and mortality rates. World Health Organization (WHO) estimated approximately 229 million cases worldwide, with children under five years old comprising 67%(274,000) of those afected, representing the most vulnerable demographic group. Despite the prevalence of malaria, existing research has not extensively explored the utilization of machine learning techniques to predict malaria outbreaks, in Nigeria. This study aims to fill this gap by employingfive supervised machine learning methods: Naive Bayes, Support Vector Machines (SVM), Linear Regression, Logistic Regression, and K-Nearest Neighbor. Utilizing meteorological and malaria incidence data spanning from 2010 to 2020, the research employed the Scikit-learn library within the Anaconda IDE, utilizing the Python programming language. Results indicate that Naive Bayes achieved the highest accuracy, with an average accuracy of 79.1%for both testing and training datasets, making it the optimal model for predicting malaria incidence outbreaks based on the dataset utilized. Following closely is Support Vector Machine (SVM) with an average accuracy of 75.45%for both testing and training data, followed by K-Nearest Neighbor with an average accuracy of 70.8%. Logistic Regression exhibited an average accuracy of 68%. However, Linear Regression, with an average accuracy of 26.05%, is not recommended for predicting malaria incidence outbreaks based on the findings ofthis research.  \nKeywords: Artificial Intelligence, Machine Learning, Malaria, Naive Bayes, Prediction, Support Vector machine  \nCARITAS UNIVERSITY JOURNALS [www.caritasuniversityjournals.org](www.caritasuniversityjournals.org)  \nCaritas Journal of Physical and Life Sciences (CJPLS 3(1), 2024) 2  \n1. INTRODUCTION  \nArtificial intelligence (AI) encompasses a branch of computer science dedicated to constructing intelligent machines capable of mimicking human cognitive functions. Among its various domains are machine learning, robotics, and knowledge representation [7] . Machine learning, a subset of AI, involves training algorithms toglean insights from past experiences and refine their performance to tackle complex problems. It has emerged as a pivotal tool in addressing challenges ranging from image and speech recognition to medical diagnosis and disease prediction.  \nThe prediction of disease outbreaks is a critical application of machine learning, offering insights into the likelihood of disease surpassing anticipated levels at specific times [5] . This predictive capacity is particularly crucial in the realm of infectious diseases, where timely detection can inform preparedness and mitigation efforts, thereby bolstering public health responses [3] . Malaria, a mosquito-borne illness caused by Plasmodium protists, poses a significant global health threat, with transmission occurring via infected mosquito bites, leading to approximately a million deaths annually, predominantly in developing countries [8] .  \nNigeria, in particular, bears a heavy burden of malaria, comprising 25% of global malaria cases and deaths in 2018 [8] . With 76% of its population residing in high-transmission areas, the country experiences varying transmission seasons across regions [9] . Given the profound impact of malaria on mortality ra","cbCaierAlNcLyach","https://ap.wps.com/l/cbCaierAlNcLyach","pdf",409667,1,9,"English","en",105,"# Abstract\n# Introduction\n## Malaria as a public health threat\n## Need for predictive models in Nigeria\n# Literature Review\n## Machine Learning\n## Supervised Learning\n## Support Vector Machine","[{\"question\":\"Which machine learning models were used to predict malaria outbreaks in Nigeria?\",\"answer\":\"The study employs Naive Bayes, Support Vector Machines (SVM), Linear Regression, Logistic Regression, and K-Nearest Neighbor (K-NN).\"},{\"question\":\"Which model performed best according to the reported accuracy results?\",\"answer\":\"Naive Bayes achieved the highest average accuracy, about 79.1% on both training and testing datasets. SVM ranked next at about 75.45%, followed by K-NN and Logistic Regression.\"},{\"question\":\"Why is Linear Regression not recommended for this prediction task based on the findings?\",\"answer\":\"Linear Regression shows an average accuracy of about 26.05%, far below the other models, indicating weak predictive performance on the utilized dataset.\"}]","The Potential of Machine Learning Techniques in Predicting Malaria Outbreaks in Nigeria - Article | PDF",1785816405,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-potential-of-machine-learning-techniques-in-predicting-malaria-outbreaks-in-nigeria-article","",{"@graph":36,"@context":86},[37,54,69],{"@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/the-potential-of-machine-learning-techniques-in-predicting-malaria-outbreaks-in-nigeria-article/123426/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning models were used to predict malaria outbreaks in Nigeria?","Question",{"text":76,"@type":77},"The study employs Naive Bayes, Support Vector Machines (SVM), Linear Regression, Logistic Regression, and K-Nearest Neighbor (K-NN).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which model performed best according to the reported accuracy results?",{"text":81,"@type":77},"Naive Bayes achieved the highest average accuracy, about 79.1% on both training and testing datasets. SVM ranked next at about 75.45%, followed by K-NN and Logistic Regression.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is Linear Regression not recommended for this prediction task based on the findings?",{"text":85,"@type":77},"Linear Regression shows an average accuracy of about 26.05%, far below the other models, indicating weak predictive performance on the utilized dataset.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"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":107,"slug":138},19,"General","general"]