A Symptom-Based Machine Learning Model for Malaria Diagnosis in Nigeria

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dc.authoridVarol, Asaf/0000-0003-1606-4079en_US
dc.authoridMuhammad, Bilyaminu/0000-0003-4281-5729en_US
dc.contributor.authorMuhammad, Bilyaminu
dc.contributor.authorVarol, Asaf
dc.date.accessioned2024-07-12T21:40:38Z
dc.date.available2024-07-12T21:40:38Z
dc.date.issued2021en_US
dc.department[Belirlenecek]en_US
dc.description9th International Symposium on Digital Forensics and Security (ISDFS) -- JUN 28-29, 2021 -- Fırat Univ, Elazig, TURKEYen_US
dc.description.abstractMalaria, with around 200 million cases worldwide, tends to kill more people than war and crises. With efforts to reduce mortality rates being futile, an inadequate malaria diagnosis is one of the barriers to a successful reduction in mortality. Machine learning methods were used to classify the stages of malaria in patients to improve diagnosis. To predict the stages of malaria, this research used knowledge of an algorithm of machine learning for a predictive model. A 77% accurate decision algorithm was developed using the symptoms of patients to identify their malaria stages. This research also discovered that malaria does not kill only children (between 0-5 years), in contrast to what has been pointed out in many research studies. This study shows that older women are more likely to experience severe stages of malaria. Therefore, adequate care should be considered for these women once they show some of the significant symptoms as described in the model. This approach applies to everyone with the symptoms set out in the model. This system will provide a preliminary test before conducting a confirmatory diagnosis in the laboratories.en_US
dc.description.sponsorshipIEEE Turkey Sect,Maltepe Univ,Sam Houston State Univ,Gazi Univ,San Diego State Univ,Arab Open Univ,Hacettepe Univ,Polytechnic Inst Cavado & Ave,Balikesir Univ,Ondokuz Mayis Univ,Assoc Software & Cyber Secur Turkey,Informat Assoc Turkey,Recep Tayyip Erdoğan Univ,Singidunum Univ,TELUQ Univ,Yıldız Teknik Univen_US
dc.identifier.doi10.1109/ISDFS52919.2021.9486315
dc.identifier.isbn978-1-6654-4481-1
dc.identifier.scopus2-s2.0-85114668140en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISDFS52919.2021.9486315
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7398
dc.identifier.wosWOS:000844418700003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof9th International Symposium on Digital Forensics And Security (Isdfs'21)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY08739
dc.subjectMalariaen_US
dc.subjectSokotoen_US
dc.subjectDecision Treeen_US
dc.subjectMachine Learningen_US
dc.titleA Symptom-Based Machine Learning Model for Malaria Diagnosis in Nigeriaen_US
dc.typeConference Object
dspace.entity.typePublication

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