ArticleClinical and experimental otorhinolaryngology2022
Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors.
Article in Clinical and experimental otorhinolaryngology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 23 citations in OpenAlex.
- Prediction of the respiratory disease incidence based on environmental factors using machine learning techniques in Penang, Malaysia.Scientific reports · 2026Article
- Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.Scientific reports · 2024Article
- Associations between Peritonsillar Abscess and Deep Neck Infection in Chronic Periodontitis Patients: Two Nested Case-Control Studies Using a National Health Screening Cohort.Journal of clinical medicine · 2024Article
- Methodological Challenges in Spatial and Contextual Exposome-Health Studies.Critical reviews in environmental science and technology · 2023Article
- Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods.Scientific reports · 2022Article
- A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome.Frontiers in nutrition · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
objectivesBecause climatic and air-pollution factors are known to influence the occurrence of respiratory diseases, we used these factors to develop machine learning models for predicting the occurrence of respiratory diseases.
methodsWe obtained the daily number of respiratory disease patients in Seoul. We used climatic and air-pollution factors to predict the daily number of patients treated for respiratory diseases per 10,000 inhabitants. We applied the relief-based feature selection algorithm to evaluate the importance of feature selection. We used the gradient boosting and Gaussian process regression (GPR) methods, respectively, to develop two different prediction models. We also employed the holdout cross-validation method, in which 75% of the data was used to train the model, and the remaining 25% was used to test the trained model. We determined the estimated number of respiratory disease patients by applying the developed prediction models to the test set. To evaluate the performance of each model, we calculated the coefficient of determination (R2) and the root mean square error (RMSE) between the original and estimated numbers of respiratory disease patients. We used the Shapley Additive exPlanations (SHAP) approach to interpret the estimated output of each machine learning model.
resultsFeatures with negative weights in the relief-based algorithm were excluded. When applying gradient boosting to unseen test data, R2 and RMSE were 0.68 and 13.8, respectively. For GPR, the R2 and RMSE were 0.67 and 13.9, respectively. SHAP analysis showed that reductions in average temperature, daylight duration, average humidity, sulfur dioxide (SO2), total solar insolation amount, and temperature difference increased the number of respiratory disease patients, whereas increases in atmospheric pressure, carbon monoxide (CO), and particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) increased the number of respiratory disease patients.
conclusionWe successfully developed models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. These models could evolve into public warning systems.
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