Key feature analysis of clinical manifestations and biochemical indicators in mycoplasma pneumonia diagnosis based on machine learning
The diagnostic process of mycoplasma pneumoniae pneumonia(MPP)cases involves a large number of clinical and biochemical indicators.Clinical physicians often face with challenges in correctly diagnosing MPP due to the lack of well-defined diagnostic indicators.In order to obtain the key clinical indicators and biochemical indicators in the diagnosis and assessment of MPP,this study employed machine learning classification models for quantitative analysis.Firstly,machine learning models were trained using 21 clinical indicators and 22 biochemical indicators.The models'accuracy,precision,recall,and F1 scores were computed to examine the performance of machine learning models for classifying MPP.Machine learning models have achieved the accuracy of 0.94 and 0.78 in clinical manifestations and biochemistry indicators,respectively.After ensuring the reliability of the models,feature importance assessment is conducted to select important features.The comprehensive result analysis showed that among the clinical manifestations data,Three Concave Sign.1,Cough Nature,Moist Crackles,Rhonchus or Wheeze,and Pleural Ef-fusion indicators contributed the most to the model classification ability.In the biochemical data,Nucleic Acid PCR,IgM(acute stage),White blood cell(WBC),CRP(C Reaction Protein),and Lymphocyte%(L)showed the greatest influence on the model′s classification.This study provides crucial indicators for the diagnosis of MPP,serving as a foundational reference for clinical diag-nosis and assessment.
万方数据
维普
