2026-05-29T23:04:33Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/243102026-02-03T10:57:01Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Guzmán-de-los-Riscos, Eduardo Francisco
author
Belmonte-Martínez, María Victoria
author
Lélis Carvalho, Viviane Marie
2022-06-07T12:27:17Z
2022-06-07T12:27:17Z
2022-03-18
Guzmán, E., Belmonte, M.-V., & Lelis, V. M. (2022). Ensemble methods for meningitis aetiology diagnosis. Expert Systems, e12996. https://doi.org/10.1111/exsy.12996
https://hdl.handle.net/10630/24310
10.1111/exsy.12996
In this work, we explore data-driven techniques for the fast and early diagnosis concerning the etiological origin of meningitis, more specifically with regard to differentiating between viral and bacterial meningitis. We study how machine learning can be used to predict meningitis aetiology once a patient has been diagnosed with this disease. We have a dataset of 26,228 patients described by 19 attributes, mainly about the patient's observable symptoms and the early results of the cerebrospinal fluid analysis. Using this dataset, we have explored several techniques of dataset sampling, feature selection and classification models based both on ensemble methods and on simple techniques (mainly, decision trees). Experiments with 27 classification models (19 of them involving ensemble methods) have been conducted for this paper. Our main finding is that the combination of ensemble methods with decision trees leads to the best meningitis aetiology classifiers. The best performance indicator values (precision, recall and f-measure of 89% and an AUC value of 95%) have been achieved by the synergy between bagging and NBTrees. Nonetheless, our results also suggest that the combination of ensemble methods with certain decision tree clearly improves the performance of diagnosis in comparison with those obtained with only the corresponding decision tree.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Meningitis
Ensemble methods for meningitis aetiology diagnosis
journal article
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
URL
https://riuma.uma.es/bitstreams/61dfa518-cb40-4342-a104-e8227a4747bd/download
File
MD5
92c0a05ba997ce9d5456b34b847c9b45
3828573
application/pdf
Expert Systems - 2022 - Guzm n - Ensemble methods for meningitis aetiology diagnosis.pdf