Figure 1 shows the flowchart of participants in our research. We identified 13,208 OED visits to the Second Affiliated Hospital of Guangzhou Medical University that had a major diagnosis of a chronic respiratory disease defined by the International Classification of Diseases, Tenth Revision, Clinical Modification code (J45.900, J44.001, J44.101, J44.803, and J98.801). The duration of the collected data lasted from January 1, 2016, to December 31, 2017, which is 731 days of continuous data. For statistical purposes, the days where the daily volume was less than 24 were labeled as nonpeak events, and the rest were labeled as peak events.

Table 1 describes the Pearson correlation coefficient between OED visit numbers and input indicators. We found that OED visit numbers showed positive correlations with wind speed, atmospheric pressure, carbon monoxide, sulphur dioxide, nitrogen dioxide, and PM25. However, OED visit numbers showed negative correlations with outdoor temperature, relative humidity, and ozone. The weather and air quality data distribution of patients with acute exacerbations of COPD from peak and nonpeak groups was shown in Table 2.

Since the effect of weather and air quality on respiratory conditions in humans was not instantaneous, representative lags were applied to variables based on the work done previously in this area [3,24-26]. To simplify the delayed impact of respiratory conditions, we considered a 3-day lag for all variables.

We removed records with less than 10 people on weekends to eliminate weekend effects, bringing the total number of samples collected to 559. To create a meaningful feature vector for training and cross-validation, the date field was removed to obtain a (X, y), where X was a matrix with the dimensions (m × n = 559 × 9) representing values of variables, and y was a vector of length (m=559) representing the output class of the examples (ie, events). Analysis of the data suggested that the output class was highly imbalanced with 413 examples of nonpeak and 146 examples of peak events.

In this section the ML algorithms are presented and discussed; details of the updating and classification processes are described in the following algorithms.

Precision, recall, and F measure are the metrics that are used to evaluate our proposed ML methods. Based on the classification of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), we have the following formulas.

We then define the F measure, a metric that balances precision and recall.

We calculate the overall accuracy, precision, recall, and F measure for nonpeak events and peak events, respectively. Evaluation of the ML approaches on the weather and air quality data are shown in Table 3. It showed that the developed random forest gave the best predictive performance. This was mainly due to the data collection fitting better with the random forest.

In addition, we used the receiver operating characteristic (ROC) curve to evaluate the multiple ML approaches on the same dataset (Table 4). We found that adaptive boosting neural networks achieved the biggest ROC area under the curve on the air quality data, tree bag on the climate data, and random forest on weather and air quality data. In general, we discovered that the predictive performance of the ML approaches improves as data variables increase.

Recent studies have shown that weather and air pollution have been a major problem leading to an increase in daily deaths and hospital admissions for chronic respiratory illnesses [3-5,27,28]. We focused the distribution of daily patient visits for 2 years (ie, 2016 and 2017) (Figure 2). It is worth noting that peak days are more dominant from October to March, which indicates that the haze is a strong predictor, as these months are mostly colder in Guangzhou. Thus, it is important to recognize the peak OED visits for respiratory conditions.

Previous studies mainly focused on the peak event forecasting ED visits for patients with one or more diseases. We expanded the study population to include outpatient visits for patients with chronic respiratory diseases. In fact, many patients with chronic respiratory diseases also seek treatment from outpatient departments. Thus, predicting the OED peak visits for chronic respiratory disease plays an important role in clinical management.

We developed a variety of learning methods to forecast the OED peak visits, from simple models to complex ensemble learning ones. In particular, the ensemble learning models achieved good prediction results. In terms of indicators, most of the previous studies used air pollution indicators to predict the peak events of ED visits; however, we used weather and air quality indicators to build a more complete set of features.

There are a few limitations to this study. In this study, we used nine variables, namely, wind speed, atmospheric pressure, outdoor temperature, relative humidity, carbon monoxide, ozone, sulphur dioxide, nitrogen dioxide, and PM25, as these variables have been associated with exacerbation of respiratory diseases. However, there are some other variables that also contribute to the exacerbation of these diseases, such as formaldehyde and nitrogen oxide [29]. The Environmental Protection Agency of Guangzhou does not disclose the daily data for variables such as formaldehyde. Other pollutants are either not measured or had too many missing values. Therefore, we were not able to include these variables in our study.

In terms of weather, Guangzhou as a coastal city in southern China has a higher air humidity than other northern cities. In terms of air pollution, some studies have shown that patients with lower levels of economics and education are more susceptible to air pollution [30]. Guangzhou has a significantly higher economic and educational level than the national average. However, the pollution of haze and the harmful emissions of Guangzhou are also serious [31]. In particular, the lighter particulate matter is higher than other northern cities due to automobile exhaust and industrial emissions. Therefore, the prediction result of this study may not be directly applicable to other regions due to the regional differences in climate and air pollution.

In this paper, we investigated ML methods to forecast the peak events of patients with chronic respiratory diseases visiting OEDs combined with nine weather and air quality predictors. Overall, random forest outperforms the other methods in the accuracy, F measure, and ROC on the validation dataset. Compared with similar studies before, we used more indicators and ML methods to study the subject and achieved good results. The ML methods may act as a useful tool to adapt medical services in advance by predicting the peak number of OED arrivals.