[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124860-en":3,"doc-seo-124860-105":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},124860,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning Model for Prediction of Malaria in Low and High Endemic Areas of Tanzania","Presumptive treatment and self-medication with anti-malaria drugs is widespread in limited-resource settings and often undermines proper malaria management through unnecessary drug use and missed non-malaria illnesses. This dissertation develops a machine-learning diagnostic model using patients’ symptoms and non-symptomatic features from low- and high-endemic areas in Tanzania. Data from 2 regions (Morogoro and Kilimanjaro) covering 2015–2019 included 2556 records and 36 features, evaluated with k-fold cross-validation. Feature selection showed region-dependent differences, with Random Forest and Decision Tree achieving high prediction accuracies (up to 99%).","MACHINE LEARNING MODEL FOR PREDICTION OF MALARIA IN LOW AND HIGH ENDEMIC AREAS OF TANZANIA  \nMartina Wilfred Mariki  \nA Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and Technology  \nArusha, Tanzania  \nABSTRACT  \nPresumptive treatment and self-medication with anti-malaria drugs is a common practice inmost limited resource settings that hinders proper management of malaria. However, these approaches have been considered unreliable due to the unnecessary use of malaria medication and untreated diseases that relate to malaria. This study aimed to develop a machine-learning model for malaria diagnosis using patients’ symptoms and non-symptomatic features in high and low endemic areas of Tanzania. The malaria diagnosis dataset with 2556 patient’s records and 36 features was collected in two regions of Tanzania: Morogoro and Kilimanjaro from 2015-2019. Machine learning classifiers with the k-fold cross-validation methods were used to train and validate the model. To improve the performance of the diagnostic model, important features for malaria diagnosis were selected, and it was observed that the ranking of features differs among regions and when combined dataset. Significant features selected are residence area, fever, age, general body malaise, visit date, and headache. Random Forest and Decision Tree algorithms were the best performing classifiers in modelling malaria diagnosis datasets and attained 96%, 99% and 98% prediction accuracy for Kilimanjaro, Combined and Morogoro dataset respectively. These best-performing classifiers were evaluated using the unseen malaria diagnosis dataset and performed well in classifying malaria patients from sick patients. The final developed model showed that only a specific combination of features can predict malaria accurately. The results of this study revealed that malaria diagnosis using patients’ symptoms and demographic features is possible. Also, the study results offer additional knowledge and shed light on the state diagnosis of malaria in the country. The developed machine learning model enables prediction of patient’s malaria state using symptoms observed and nonsymptomatic features before prescription of anti-malaria drugs. Apart from that the output of this study will be a necessary step in designing a malaria diagnosis decision support system through the developed model. Furthermore, towards reducing drug resistance, the results of this study can be used by the policymakers and the Ministry of Health for better management of malaria disease in health facilities and drug dispensing outlets to avoid self-medication and presumptive treatment.  \nDECLARATION  \nI, Martina Wilfred Mariki, do hereby declare to the Senate of the Nelson Mandela African Institution of Science and Technology Arusha that this dissertation is my original work and that it has neither been submitted nor is concurrently submitted for degree award in any other institution.  \nMartina Wilfred Mariki  \n\n| Name of Candidate\u003Cbr>Dr. Elizabeth Mkoba | Signature\u003Cbr>The above declaration is confirmed by: | Date |\n| --- | --- | --- |\n| Name of Supervisor 1\u003Cbr>Dr. Neema Mduma | Signature | Date |\n\nName of Supervisor 2  \nSignature  \nDate  \nCOPYRIGHT  \nThis dissertation is copyright material protected under the Berne Convention, the Copyright Act of 1999 and other international and national enactments, on that behalf, on intellectual property. It must not be reproduced by any means, in full or in part, except for short extracts in fair dealing; for researcher private study, critical scholarly review or discourse with an acknowledgement, without the written permission of the office of Deputy Vice-Chancellor for Academic, Research and Innovation on behalf of both the author and the Nelson Mandela African Institution of Science and Technology.  \n.  \nCERTIFICATION  \n","cbCaiegI8OcgXBjg","https://ap.wps.com/l/cbCaiegI8OcgXBjg","pdf",4000991,1,148,"English","en",105,"## Abstract\n## Declaration\n## Copyright\n## Certification\n## Acknowledgements\n## Dedication\n## Table of Contents","[{\"question\":\"What problem does the study address in malaria management?\",\"answer\":\"The study targets unreliable presumptive treatment and self-medication that lead to unnecessary anti-malaria drug use and untreated diseases related to malaria.\"},{\"question\":\"What data and features are used to build the machine-learning model?\",\"answer\":\"The model uses patients’ symptoms and non-symptomatic features collected from Morogoro and Kilimanjaro between 2015 and 2019, using 2556 patient records and 36 features.\"},{\"question\":\"Which classifiers performed best, and what accuracy did they achieve?\",\"answer\":\"Random Forest and Decision Tree were the best-performing classifiers, reaching 96% (Kilimanjaro), 99% (combined), and 98% (Morogoro) prediction accuracy.\"}]","Machine Learning Model for Prediction of Malaria in Low and High Endemic Areas of Tanzania | 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