  Artificial intelligence methods have been increasingly turning into a
potentially powerful tool in the diagnosis and management of diseases. In this
study, we utilized logistic regression (LR), decision tree (DT), gradient
boosted decision tree (GBDT), support vector machine (SVM), and multilayer
perceptron (MLP) as machine learning models to rapidly diagnose the mycoplasma
pneumoniae pneumonia (MPP) in children patients. The classification task was
carried out after applying the preprocessing procedure to the MPP dataset. The
most efficient results are obtained by GBDT. It provides the best performance
with an accuracy of 93.7%. In contrast to standard raw feature weighting, the
feature importance takes the underlying correlation structure of the features
into account. The most crucial feature of GBDT is the "pulmonary infiltrates
range" with a score of 0.5925, followed by "cough" (0.0953) and "pleural
effusion" (0.0492). We publicly share our full implementation with the dataset
and trained models at https://github.com/zhenguonie/2021_AI4MPP.
