Machine learning-assisted prediction of pneumonia based on non-invasive measures
non-invasive measures
Pneumonia
3. Good health
Machine Learning
03 medical and health sciences
machine learning
0302 clinical medicine
electronic health records (EHR)
pneumonia
Humans
Public Health
Public aspects of medicine
RA1-1270
decision support system (DSS)
DOI:
10.3389/fpubh.2022.938801
Publication Date:
2022-07-28T10:12:26Z
AUTHORS (8)
ABSTRACT
Pneumonia is an infection of the lungs that characterized by high morbidity and mortality. The use machine learning systems to detect respiratory diseases via non-invasive measures such as physical laboratory parameters gaining momentum has been proposed decrease diagnostic uncertainty associated with bacterial pneumonia. Herein, this study conducted several experiments using eight models predict pneumonia based on biomarkers, parameters, features. We perform machine-learning analysis 535 different patients, each 45 Data normalization rescale all real-valued features was performed. Since it a binary problem, we categorized patient into one class at time. designed three evaluate models: (1) feature selection techniques select appropriate for models, (2) imbalanced original dataset, (3) SMOTE data. then compared their effectiveness in predicting Biomarkers C-reactive protein procalcitonin demonstrated most significant discriminating power. Ensemble RF (accuracy = 92.0%, precision 91.3%, recall 96.0%, f1-Score 93.6%) XGBoost 90.8%, 92.6%, 92.3%, f1-score 92.4%) achieved highest performance accuracy dataset AUCs 0.96 0.97, respectively. On prediction results f1-scores 92.0 91.2%, Also, AUC 0.97 both models. Our showed diagnosis pneumonia, individual clinical history, indicators, symptoms do not have adequate discriminatory can also conclude ensemble ML performed better study.
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