Abstract
Meningitis is an infectious disease that can lead to neurocognitive impairments due to an inflammatory process in the meninges caused by various agents, mainly viruses and bacteria. Early diagnosis, especially when dealing with bacterial meningitis, reduces the risk of complications and mortality. Able to identify the most relevant features in the early diagnosis of bacterial meningitis. The model is designed to explore the prediction of specific data through the Logistic Regression, K Nearest Neighbour, and Random Forest algorithms. Early identification of the patient’s clinical evolution, cure, or death is essential to offer more effective and agile therapy. Random Forest Algorithm is the best performing algorithm with 90.6% accuracy, Logistic Regression with 90.3% performance and KNN with 90.1%. The most relevant characteristics to predict deaths are low education level and red blood cells in the CSF, suggesting intracranial haemorrhage. The best-performing algorithm will predict the evolution of the clinical condition that the patient will present at the end of hospitalisation and help health professionals identify the most relevant characteristics capable of predicting an improvement or worsening of your general clinical condition early on.
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Pinheiro, P.G.C.D. et al. (2023). An Application of Machine Learning in the Early Diagnosis of Meningitis. In: Visvizi, A., Troisi, O., Grimaldi, M. (eds) Research and Innovation Forum 2022. RIIFORUM 2022. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-19560-0_7
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