A resarch paper on forecasting meningitis with machine learning written by B. Dobardzic, A. Alibasic, N. Milosevic, B. Malisic and M. Vukotic just appeared in the Proceedings of the Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and International Conference on Medical and Biological Engineering (CMBEBIH), September 14–16, 2023, Sarajevo, Bosnia and Herzegovina—Volume 1: Imaging, Engineering and Artificial Intelligence in Healthcareat the following link.
Abstract – Meningitis is a life-threatening disease that can lead to severe neu-
rological damage and death if not diagnosed and treated in a timely manner.
In this study, the application of machine learning methods to create a predic-
tive model for meningitis diagnosis based on clinical signs, blood, protein, and
other health parameters is explored. Our goal is to determine the most reliable
and accurate method of meningitis prediction. We analyze a sizable dataset of
meningitis patients using cutting-edge classification techniques, such as Support
Vector Machines and Random Forest. Findings have shown that machine learning
techniques can accurately estimate a patient’s risk of meningitis. The importance
of features for meningitis diagnosis is determined by evaluating them, and the
effectiveness of various models is also compared.
