PREDICTION OF MALARIA SPREAD IN THE UNIVERSITY OF PORT HARCOURT USING MACHINE LEARNING TECHNIQUES
Abstract
Thisprojectexplorestheapplicationofmachinelearningtechniquesforpredictingmalaria
rigorousevaluation,themodeldemonstratedapredictionaccuracyof70%,highlighting
historicalmalariaincidencerates.TheRandomForestMLtechniquewaschosenasthe
abilitytomaintainabalancebetweenaccuracyandinterpretability.Ontheresultsof
spreadattheUniversityofPortHarcourt.Asystematicmethodwasused,starting
withthecollectionandpreprocessingofrelevantdata,includingclimaticvariablesand
predictivemodelduetoitsrobustperformanceinhandlingcomplexdatasetsandits
itspotentialasareliabletoolforforecastingmalariaoutbreaks.
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