Clin Exp Otorhinolaryngol.  2022 May;15(2):168-176. 10.21053/ceo.2021.01536.

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

• Ku Y
• Kwon SB
• Yoon JH
• Mun SK
• Chang M

• KMID: 2530163
• DOI: http://doi.org/10.21053/ceo.2021.01536

Abstract

Objectives
. Because 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.
Methods
. We 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.
Results
. Features 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 (PM_2.5) increased the number of respiratory disease patients.
Conclusion
. We successfully developed models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. These models could evolve into public warning systems.

Keyword

Machine Learning; Respiratory Diseases; Climate; Air Pollution; Gradient Boosting; Gaussian Process Regression

Figure

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  Fig. 1. Daily numbers of patients treated for respiratory disease (RD) per 10,000 inhabitants in Seoul from January 1, 2014, to December 31, 2019. The green dots indicate holidays, the blue dots, the days after holidays, and black dots, regular weekdays.

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  Fig. 2. The overall procedure for the development of the machine learning (ML) prediction models. The training and test sessions with hyperparameter optimization were performed after data preprocessing and feature selection, after which Shapley Additive exPlanations (SHAP)-based interpretation for the developed models was performed.

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  Fig. 3. The results of the relief-based feature selection algorithm for 15 climatic and air-pollution factors. Higher feature weights indicate higher importance for the target response.

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  Fig. 4. The prediction results of the daily number of respiratory disease (RD) patients using unseen test data (the latter part of 2018 and 2019). (A) The prediction results using the developed gradient boosting model. (B) The prediction results using the developed Gaussian process regression (GPR) model. The black and blue dots indicate the actual and predicted daily numbers of RD patients per 10,000 inhabitants, respectively. The shaded area represents the 95% confidence interval. (C, D) Scatter plots between the actual and predicted RD patients for the developed gradient boosting and GPR models, respectively. The solid black line represents the Y=X line.

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  Fig. 5. Shapley Additive exPlanations (SHAP) feature importance (A) and summary plot (B). The SHAP feature importance (i.e., the mean absolute Shapley values) for the gradient boosting model. In the SHAP summary plot, the features on the Y-axis are ordered based on their importance. The color bars indicate the amplitudes of feature values from low to high. Overlapping points are stacked in the Y-axis directions of both images to show the distribution of the Shapley values for each feature. 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. NO2, nitrogen dioxide; PM10, particulate matter ≤10 µm in aerodynamic diameter.

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