Performance of selected BIOTOPE-trained random forest classifier compared with WHO, RISC-Malawi, RISC, PERCH and PREPARE methods on BIOTOPE training data (A,B) and on BIOTOPE testing data (C,D).

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healthcare

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model

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PatMohMasBil

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Performance of selected BIOTOPE-trained random forest classifier compared with WHO, RISC-Malawi, RISC, PERCH and PREPARE methods on BIOTOPE training data (A,B) and on BIOTOPE testing data (C,D).

Predicting Hospitalised Paediatric Pneumonia Mortality Risk: An External Validation of RISC and mRISC, and Local Tool Development (RISC-Malawi) from Malawi

Random Forest plot.

Background

Neglected Tropical Diseases (NTDs) affect 1.5 billion people worldwide with

Who Stunting data ethiopia.

Background

The prevalence of under-five stunting in Ethiopia remains above 30%, which,

Notes on data extraction.

Background

Malaria and schistosomiasis are co-endemic across sub-Saharan Africa, where

Comparison of random forest model ROC curve.

Understanding the time of the menstrual cycle would help women to avoid getting pregnant wit

Hyperparameter tuning of the random forest model.

The incidence of maternal deaths from preventable pregnancy-related conditions remains alarm

<p>Performance of selected BIOTOPE-trained random forest classifier compared with WHO, RISC-Malawi, RISC, PERCH and PREPARE methods on BIOTOPE training data (A,B) and on BIOTOPE testing data (C,D).</p>

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Predicting Hospitalised Paediatric Pneumonia Mortality Risk: An External Validation of RISC and mRISC, and Local Tool Development (RISC-Malawi) from Malawi

<p>Random Forest plot.</p>

<p>Who Stunting data ethiopia.</p>

<p>Notes on data extraction.</p>

<p>Comparison of random forest model ROC curve.</p>

<p>Hyperparameter tuning of the random forest model.</p>