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. 2024 Feb 13;23(1):46.
doi: 10.1186/s12936-024-04869-3.

A machine learning approach for early identification of patients with severe imported malaria

Alessandra D'Abramo #  1 , Francesco Rinaldi #  2 , Serena Vita  3 , Riccardo Mazzieri  4 , Angela Corpolongo  1 , Claudia Palazzolo  1 , Tommaso Ascoli Bartoli  1 , Francesca Faraglia  1 , Maria Letizia Giancola  1 , Enrico Girardi  1 , Emanuele Nicastri  1
Affiliations

A machine learning approach for early identification of patients with severe imported malaria

Alessandra D'Abramo et al. Malar J. .

Abstract

Background: The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting.

Methods: This is a single-centre cross-sectional study, patients with confirmed malaria, consecutively hospitalized to the Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy from January 2007 to December 2020, were recruited. Different ML approaches were used to perform the analysis of this dataset: support vector machines, random forests, feature selection approaches and clustering analysis.

Results: A total of 259 patients with malaria were enrolled, 89.5% patients were male with a median age of 39 y/o. In 78.3% cases, Plasmodium falciparum was found. The patients were classified as severe malaria in 111 cases. From ML analyses, four parameters, AST, platelet count, total bilirubin and parasitaemia, are associated to a negative outcome. Interestingly, two of them, aminotransferase and platelet are not included in the current list of World Health Organization (WHO) criteria for defining severe malaria.

Conclusion: In conclusion, the application of ML algorithms as a decision support tool could enable the clinicians to predict the clinical outcome of patients with malaria and consequently to optimize and personalize clinical allocation and treatment.

Keywords: Imported malaria; Machine learning; Risk factors; Severe malaria.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Machine learning workflow
Fig. 2
Fig. 2
Cross validation accuracy obtained with increasing number of features
See this image and copyright information in PMC

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