Atropine is a common treatment used in children with myopia. However, it probably affects intraocular pressure (IOP) under some conditions. Our research aims to analyze clinical data by using machine learning models to evaluate the effect of 19 important factors on intraocular pressure (IOP) in children with myopia treated with topical atropine. The data is collected on 1545 eyes with spherical equivalent (SE) less than −10.0 diopters (D) treated with atropine for myopia control. Four machine learning models, namely multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost), were used. Linear regression (LR) was used for benchmarking. The 10-fold cross-validation method was used to estimate the performance of the five methods. The main outcome measure is that the 19 important factors associated with atropine use that may affect IOP are evaluated using machine learning models. Endpoint IOP at the last visit was set as the target variable. The results show that the top five significant variables, including baseline IOP, recruitment duration, age, total duration and previous cumulative dosage, were identified as most significant for evaluating the effect of atropine use for treating myopia on IOP. We can conclude that the use of machine learning methods to evaluate factors that affect IOP in children with myopia treated with topical atropine is promising. XGBoost is the best predictive model, and baseline IOP is the most accurate predictive factor for endpoint IOP among all machine learning approaches.
Influenza is a serious public health issue, as it can cause acute suffering and even death, social disruption, and economic loss. Effective forecasting of influenza outpatient visits is beneficial to anticipate and prevent medical resource shortages. This study uses regional data on influenza outpatient visits to propose a two-dimensional hierarchical decision tree scheme for forecasting influenza outpatient visits. The Taiwan weekly influenza outpatient visit data were collected from the national infectious disease statistics system and used for an empirical example. The 788 data points start in the first week of 2005 and end in the second week of 2020. The empirical results revealed that the proposed forecasting scheme outperformed five competing models and was able to forecast one to four weeks of anticipated influenza outpatient visits. The scheme may be an effective and promising alternative for forecasting one to four steps (weeks) ahead of nationwide influenza outpatient visits in Taiwan. Our results also suggest that, for forecasting nationwide influenza outpatient visits in Taiwan, one- and two-time lag information and regional information from the Taipei, North, and South regions are significant.
Developing an effective interval-valued time series (ITS) forecasting scheme for electric power generation is an important issue for energy operators and governments when making energy strategic decisions. The existing studies for ITS forecasting only consider basic descriptive information such as center, radius, upper and lower bounds, and overlooks the distribution information within the data interval. In this study, an interval-valued time series forecasting scheme based on probability distribution information features of interval-valued data with machine learning algorithms is proposed to enhance electric power generation forecasting. In the proposed scheme, the central tendency features and dispersion features from the interval-valued data are designed as integrated features sets (IFS) and used as predictor variables. Three methods including supper vector regression and extreme learning machine and multivariate adaptive regression splines based on the IFS are utilized to develop ITS forecasting models. The daily time series of the metered generation from the Australian Energy Market Operator is used to illustrate the proposed scheme. Empirical results show that the proposed ITS forecasting schemes with IFS outperform the eight benchmark models and thus validate that the proposed scheme is an effective alternative for interval-valued electric power generation forecasting.
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