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oai:figshare.com:article/33975485 2026-09-23T17:29:23Z category_21 category_734 category_931 category_64 category_132 portal_5 item_type_3 month_year_09_2026

<p>Comparison of Recent Studies on Malaria Risk Analysis and Prediction Using Survey Data in Nigeria.</p> Ashiqur Rahman Khan (21567978) Nafisa Mahbub (20043948) Ridwan Al Aziz (20313520) Mahamudul Hassan Siddique (25099061) Zahidul Islam Sayeem (25099064) Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Infectious Diseases statistical dependency testing particularly among children namely logistic regression hinder early treatment enable timely screening agglomerative hierarchical clustering 43 candidate variables malaria risk prediction global malaria burden specific analyses demonstrated improves predictive performance highest test accuracy excluding regional identifiers automated machine learning h2o automl provides div >< p h2o glm provided xgboost performed best h2o automl malaria remains xgboost ), specific modeling regional information performed using oriented performance automated framework substantial proportion subsequently reduced socioeconomic data silhouette score saharan africa proposed framework population heterogeneity overall dataset negative results limited settings important predictors fold cross five years findings demonstrate confirmatory microscopy best recall based analysis 54 %). 3939 ). 38 %) 31 %), 25 %). <p>Comparison of Recent Studies on Malaria Risk Analysis and Prediction Using Survey Data in Nigeria.</p> 2026-09-23T17:28:50Z Dataset Dataset 10.1371/journal.pdig.0001736.t001 https://figshare.com/articles/dataset/_p_Comparison_of_Recent_Studies_on_Malaria_Risk_Analysis_and_Prediction_Using_Survey_Data_in_Nigeria_p_/33975485 CC BY 4.0