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

TL;DR

This study applies various machine learning models, notably gradient boosted decision trees, to rapidly diagnose Mycoplasma pneumoniae pneumonia in children, achieving high accuracy and identifying key clinical features.

Contribution

It introduces an AI-based diagnostic approach for pediatric MPP using multiple ML models, with GBDT showing superior performance and feature importance analysis.

Findings

GBDT achieved 93.7% accuracy in diagnosis.

Pulmonary infiltrates range is the most important feature.

The study provides publicly available dataset and models.

Abstract

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.