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Background With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency. Method Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels. Result The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year. Conclusion Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

Extreme weather events pose significant risks to power systems, necessitating effective risk forewarning and management strategies. A few existing researches have concerned the correlation of the extreme weather in different regions of power system, and traditional operation risk assessment methods gradually cannot satisfy real-time requirements. This motivates us to present an intelligent risk forewarning method for the operation of power systems considering multi-region extreme weather correlation. Firstly, a novel multi-region extreme weather correlation model based on vine copula is developed. Then, a risk level classification method for power system operations is introduced. Further, an intelligent risk forewarning model for power system operations is proposed. This model effectively integrates the multi-region extreme weather correlation and the risk level classification of the system. By employing the summation wavelet extreme learning machine, real-time monitoring and risk forewarning of the system’s operational status are achieved. Simulation results show that the proposed method can rapidly identify potential risks and provides timely risk forewarning information, helping enhance the resilience of power system operations.

The heart is one of the most vital organs in our body and crucial for proper bodily function, an unfit heart can seriously affect fitness, lifestyle and severely decrease the expected lifetime of an individual making a healthy heart necessary for survival. An early detection system for signs of a heart attack must be implemented in light of the alarming rise in the number of heart attacks in children and young adults. There has to be a method in place that is both convenient and accurate in forecasting the likelihood of a cardiac condition for the average person, such as the ECG. It has recently become easier to predict heart attacks due to machine learning (ML). Traditional prediction models and methodologies, on the other hand, are inadequate for gathering fundamental data because of their inability to imitate the high quality of mapping negative medical features. We forecast the survival of a cardiac patient using enhanced machine learning. Predicting a patient's risk of mortality from heart failure is based on information such as gender, age, blood pressure, kind of job, blood glucose, and body mass index. Support Vector Machine (SVM), Random Forest (RF), Navies Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) are just a few of the machine learning-based classification algorithms that have been built and tested. The results of the experiments show that using 80% training and 20% testing, SVM can predict heart disease with an accuracy of 96.0%.

Wetlands are recognized as one of the most important natural environments for humans. At the same time, heavy metal pollution has an important impact on wetlands. China's Raoyanghe Wetland is one of the most important natural wild species gene banks in China. Eight heavy metal elements (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in surface layer and deep layer soils were analyzed using statistical-, pollution index-, and Nemerow index-based methods, the Hakanson potential ecological risk index method, and principal component and cluster analyses. The results showed that the maximum concentrations of heavy metals exceeded the background values in the core area and buffer zone of the wetland, but the heavy metal content of the soils was generally low and did not exceed 30%. With the exception of Hg, heavy metal concentrations showed strong spatial differentiation. The differences between the surface layer and deep layer soils of the core area were smaller than in the buffer zone. With the exception of Cd, a clear vertical zonation in the buffer zone soils was observed, showing greater evidence of external influences in this zone than the core. With the exception of partial surface soils, which indicated a safe level of pollution in the core area, all other soils were classified as having a 'mild' level of pollution. Thus, the wetland is moderately polluted, with both the core area and the buffer zone presenting a low level of potential ecological risk. According to the results of the present study, heavy metal contaminants in the wetland soils were found to be derived mainly from the natural sources.

Urban flood risk assessment plays a crucial role in disaster risk reduction and preparedness. It is essential to mitigate flood risks and establish a comprehensive analysis of factors influencing flood risk, as well as classify risk levels, in order to provide a clear model for risk assessment. This article aims to propose an efficient assessment method that can classify urban flood risk levels and assist cities in flood risk management, particularly in identifying high-risk areas. The study area chosen for this method is the municipal district of Fuzhou City, located in Fujian Province, China. The proposed method utilizes the Urban Flood Risk Assessment Index, which is developed based on the risk-vulnerability framework. It integrates the combinatorial empowerment method, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and the K-means algorithm to cluster the quantitative risk factors, enabling a comprehensive analysis of the risk results. The findings demonstrate that areas characterized by intense extreme rainfall, lower elevation, gradual slope, high runoff coefficient, and high population density tend to exhibit higher flood risk. Moreover, the dominant factors contributing to high risk in different regions vary. The results obtained from this method align well with the distribution of historical flood points, indicating the effectiveness of the risk map prepared using this approach. In comparison to the results obtained from the single clustering method and the TOPSIS method used in traditional risk assessment, the proposed method can successfully identify high-risk urban flood areas. Consequently, this method offers a valuable scientific basis for urban flood prevention and control planning.

Objectives This study aims to retrospectively analyze the clinical features of Stanford type A acute aortic dissection (TAAAD) based on Sun’s modified classification, and to investigate whether the Sun’s modified classification can be used to assess the risk of preoperative rupture. Methods Clinical data was collected between January 2018 and June 2019. Data included patient demographics, history of disease, type of dissection according to the Sun’s modified classification, time of onset, biochemical tests, and preoperative rupture. Results A total of 387 patients with TAAAD who met the inclusion criteria of Sun’s modified classification were included. There were more complex types, with 75, 151 and 140 patients in the type A1C, A2C and A3C groups, respectively. The age of the entire group of patients was 51.46 ± 12.65 years and 283 (73.1%) were male. The time from onset to the emergency room was 25.37 ± 30.78 h. There were a few cases of TAAAD combined with stroke, pericardial effusion, pleural effusion, and lower extremity and organ ischemia in the complex type group. The white blood cell count (WBC), neutrophil count (NEC) and blood amylase differed significantly between the groups. Three independent risk factors for preoperative rupture were identified: neutrophil count, blood potassium ion level, and platelet count. Binary logistic regression analysis showed that the Sun’s modified classification could not be used to assess the risk of preoperative rupture in TAAAD. Conclusion TAAAD was classified as the complex type in most patients. WBC, NEC and blood amylase were significantly different between the groups. NEC and serum potassium ion level were independent risk factors for preoperative rupture of TAAAD, while platelet count was its protective factor. More samples are needed to determine whether Sun’s modified classification can be used to evaluate the risk of preoperative rupture.

The rockburst hazard induced by the extreme release of the stress concentrated in rock mass in deep underground mines poses a significant threat to the safety and economy of the mining projects. Therefore, properly managing this hazard is critical for ensuring rock engineering projects’ sustainability. This study proposes comprehensible and practical classifiers for rockburst risk level appraisal by hybridizing K -means clustering with gene expression programming, GEP, logistic regression, LR, and classification and regression tree, CART (i.e., K -mean-GEP-LR and K -means-CART classifiers). A database containing 246 rockburst events with four risk levels of none, light, moderate, and severe was compiled from previous practices. Preliminary statistical analyses were conducted to detect the extreme outliers and determine the critical rockburst indicators. The K -means clustering analysis was performed to identify the main clusters within the database and relabel the rockburst events. The GEP algorithm was then utilized to develop binary models for predicting the occurrence of each class. Then, the likelihood of each class occurrence was determined using LR. Furthermore, the K -means clustering was combined with the CART algorithm to provide another visual tree structure model. The classifiers’ performance evaluation showed 96% and 95% accuracy values in the training and testing stages, respectively, for the K -means-GEP-LR model, while the accuracy values of 98.8% and 93.0% were obtained for the foregoing stages for the K -means-CART classifier. The results showed the robustness and high classification capability of both models. MatLab codes were also provided for the K -means-GEP-LR model, which assists other researchers/engineers in implementing the model in practice.

Anomalous precipitation, with unexpected intensity and/or spatiotemporal structures, makes flood risk management in the East Asian monsoon regions challenging, where interacting circulation systems generate highly variable precipitation events. We develop an event‐level quantitative framework based on a novel lightweight Trans‐Unet model and apply it to the Hanjiang River Basin as proof‐of‐concept using a 59‐year, 1‐km daily precipitation data set. The framework reveals four patterns: West‐type (37.0% of events), Middle‐type (25.3%), Southeast‐type (21.7%), and East‐type (16.0%). West‐type events show bimodal seasonality in July and September, with July peaks linked to ∼20% fewer September events, suggesting potential implications for adaptive reservoir regulation. A probabilistic analysis further quantifies the influence of synoptic drivers on anomalies, for example, a westward extension of the western North Pacific Subtropical High, reaching as far as Taiwan, increases West‐type frequency by ∼23%, while a northward‐displaced, intensified East Asian jet at 200 hPa over northern China enhances occurrence by ∼72%.