Decision Support Models to Assist in the Diagnosis of Meningitis

Lelis, Viviane Maria; Belmonte, María‐Victoria; Guzmán, Eduardo

doi:10.1007/978-3-030-03667-6_35

Cited by 2 publications

(5 citation statements)

References 17 publications

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“…Note also that, for the first technique, the table only shows the ranking obtained with NBTree classifier, but results with other classifiers are aligned with it. This conclusion is consistent with the results concerning feature selection we obtained in previous experiments (Lélis et al, 2018), where attribute relevance was explored in a set of patient cases from 2007 to 2013.…”

Section: Methodssupporting

confidence: 92%

“…In our previous studies we developed two machine learning models which are able to perform a non-invasive diagnosis of meningitis (Lélis et al, 2017) and to determine whether or not it may be the meningococcal disease (Lélis et al, 2018) based only on observable symptoms. The goal of the study described in this paper is to develop a model able to identify the probable etiological origin of meningitis among the most frequent causal agents: bacteria or viruses.…”

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Ensemble methods for meningitis aetiology diagnosis

2022

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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.

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confidence: 92%

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Ensemble methods for meningitis aetiology diagnosis

2022

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“…This section summarises the construction process of our three decision models, described in detail in [42], [43]. Decision Model 1 (DM1) determines, only in terms of observable symptoms, whether or not the patient has meningitis; Decision Model 2 (DM2), using the same input symptoms, predicts the probability of having meningococcal meningitis; and finally, Decision Model 3 (DM3) explores the aetiology of the disease employing some chemical and cytological test data.…”

Section: ) Decision Models Constructionmentioning

confidence: 99%

“…For DM1, all the classifiers showed good performance with ROC values over 0.8 ( Table 6). The ADTree was selected because it exhibited the best results, with a ROC area value of 0.87 [43]. Fig.…”

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confidence: 99%

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Non-Invasive Meningitis Diagnosis Using Decision Trees

2020

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Meningitis is one of the pandemic diseases that many less developed countries suffer, primarily due to the lack of economic resources to face it. The more severe types of meningitis, Meningococcal Disease, MD, demand immediate medical attention since delays increase the risk of mortality. This paper presents an open and integrated Clinical Decision Support System to assist physicians in the different stages of meningitis diagnostics through observable symptoms. Our system integrates three intelligent components which try to give support to physicians in early diagnostics of meningitis. These components are based on interpretable tree-based machine learning models and knowledge-engineering techniques. A dataset of 26,228 records of patients with a meningitis diagnosis in Brazil was used to construct and evaluate the system. The performance indicators of the decision models exhibit an outstanding classification performance for MD meningitis with a classification accuracy of 94.3%. In order to test the correct diagnosis of the system, an evaluation study with real patients' data was performed. The experimental results concluded that excluding meningitis cases based only on observable symptoms is much more complicated than diagnosing it. However, the system properly diagnosed 88% of meningitis cases from the real database. INDEX TERMS Medical diagnosis, meningitis diagnostic models, tree-based machine learning.

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Decision Support Models to Assist in the Diagnosis of Meningitis

Cited by 2 publications

References 17 publications

Ensemble methods for meningitis aetiology diagnosis

Ensemble methods for meningitis aetiology diagnosis

Non-Invasive Meningitis Diagnosis Using Decision Trees

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