Specialist hybrid models with asymmetric training for malaria prevalence prediction.

ABSTRACT: Malaria is a common and serious disease that primarily affects developing countries and its spread is influenced by a variety of environmental and human behavioral factors; therefore, accurate prevalence prediction has been identified as a critical component of the Global Technical Strategy for Malaria from 2016 to 2030. While traditional differential equation models can perform basic forecasting, supervised machine learning algorithms provide more accurate predictions, as demonstrated by a recent study using an elastic net model (REMPS). Nevertheless, current short-term prediction systems do not achieve the required accuracy levels for routine clinical practice. To improve in this direction, stacked hybrid models have been proposed, in which the outputs of several machine learning models

SUBMITTER: Fisher T 

PROVIDER: S-EPMC10552258 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature