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Artificial intelligence methods have been increasingly turning into apotentially powerful tool in the diagnosis and management of diseases. In thisstudy, we utilized logistic regression (LR), decision tree (DT), gradientboosted decision tree (GBDT), support vector machine (SVM), and multilayerperceptron (MLP) as machine learning models to rapidly diagnose the mycoplasmapneumoniae pneumonia (MPP) in children patients. The classification task wascarried out after applying the preprocessing procedure to the MPP dataset. Themost efficient results are obtained by GBDT. It provides the best performancewith an accuracy of 93.7%. In contrast to standard raw feature weighting, thefeature importance takes the underlying correlation structure of the featuresinto account. The most crucial feature of GBDT is the "pulmonary infiltratesrange" with a score of 0.5925, followed by "cough" (0.0953) and "pleuraleffusion" (0.0492). We publicly share our full implementation with the datasetand trained models at https://github.com/zhenguonie/2021_AI4MPP.

Artificial Intelligence Enhanced Rapid and Efficient Diagnosis of Mycoplasma Pneumoniae Pneumonia in Children Patients