Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Insight into the effect of geographic location and intercropping on contamination characteristics and exposure risk of phthalate esters(PAEs)in tea plantation soils

Phthalate esters(PAEs)are an emerging pollutant due to widespread distribution in environmental mediums that have attracted widespread attention over recent years.However,there is little information about tea plantation soil PAEs.A total of 270 soil samples collected from 45 tea plantations in the major high-quality tea-producing regions of Jiangsu,Zhejiang,and Anhui provinces in China were analyzed for seven PAEs.The detection frequency of PAEs in tea plantation soil was 100%.DBP,DEHP,and DiBP were the main congeners in tea plantation soil.The PAEs concentrations in the upper soil were significantly higher than those in the lower soil.The concentration of tea plantation soil PAEs in Jiangsu Province was significantly lower than those in Zhejiang and Anhui provinces.Intercropping with chestnuts can effectively reduce the contamination level of PAEs in tea plantation soil.Correlation analysis,redundancy analysis,partial correlation analysis,and structural equation modeling methods further confirmed the strong direct influence of factors such as chestnut–tea intercropping,temperature,and agricultural chemicals on the variation of PAEs in tea plantation soil.The health and ecological risk assessments indicated that non-carcinogenic risk was within a safe range and that there was a high carcinogenic risk via the dietary pathway,with DBP posing the highest ecological risk.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Construction and validation of a risk-prediction model for anastomotic leakage after radical gastrectomy: A cohort study in China

Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.

Coastal erosion risk assessment of Hainan Island, China

Coastal erosion on islands is increasing due to sea level rise,frequent extreme events,and anthropogenic activities.However,studies on the multifactorial coastal erosion risk and the vulnerability of islands are limited.In this study,the Coastal Erosion Risk Assessment(CERA)method was applied for the first time to the study area in China to assess the erosion risk on the coast of Hainan Island;to explore the effects of coastal ocean dynamics,sediment movement characteristics,and anthropogenic construction;and to discuss the suitability of the method and countermeasures for coastal protection.The results show that the coast of Hainan Island shows high sensitivity,high value,low exposure,and moderate erosion.The whole island showed high vulnerability but low erosion risk,with the eastern region being more affected by erosion,particularly the eastern side of Wulong Port and Yalin Bay in Wenchang,and the shore section of Yalong Bay in Sanya,having a very high risk of coastal erosion.In addition,Monte Carlo simulation was used to check the applicability of the CERA method,and it was found that the rate of shoreline change,population density,and number of storms significantly contributed to coastal erosion,but only the short-term effects of sea level rise were considered.The effects of sea level rise and sediment grain size were primarily analyzed as influencing factors.The effects of sea level rise continue to strengthen,with coastal retreat expected to be greater than 2 m by the mid-21st century.Moreover,Hainan Island is primarily composed of the fine and medium sand types,which have little resistance to coastal erosion.Currently,the impact of sediment grain size is rarely considered in coastal erosion risk assessment studies.However,it can be incorporated into the indicator system in the future,and the spatial variation of indicators can be fully considered to strengthen the refinement study.

Research on Risk Assessment Method of Long-Tube Trailer Road Transportation

Road transport safety has always been paid attention to by the safety production managers of enterprises. In this study, cloud model and analytic hierarchy process were applied to the safety of long-tube trailer transport. The opinions of 30 experts were analyzed, from which 29 key parameters were selected. The study addressed the relevance of the parameters and the possibility of automatic collection and transmission to obtain 12 core risk factors. The macro-safety risk indicator system for long-tube trailers was established based on the identified risk indicators. Finally, a risk assessment model for road transport of long tube trailers consisting of 3 dimensions of likelihood, severity and sensitivity was constructed. This model provides a technical method for strengthening the risk control of road transport of long-tube trailers.

Landslide Hazard and Risk Assessment on the Northern Slope of Mt. Changbai, China

Landslide hazard and risk assessment on the northern slope of Mt. Changbai, a well-known tourist attraction near the North Korean-Chinese border, are assessed. This study is divided into two parts, namely, landslide hazard zonation and risk assessment. The 1992 Anbalagan and Singh method of landslide hazard zonation （LHZ） was modified and used in this area. In this way, an Associative Analysis Method was used in representative areas to get a measure for controlling factors （slope gradient, relative relief, vegetation, geology, discontinuity development, weak layer thickness and ground water）. For the membership degree of factor to slope failure, the middle range of limited values was used to calculate LHZ. Based on an estimation of the potential damage from slope failure, a reasonable risk assessment map was obtained using the relationship of potential damage and probable hazard to aid future planning and prediction and to avert loss of life.

A Systematic Assessment of Blood Lead Level in Children and Associated Risk Factors in China

In this study, we searched multiple databases for all relevant original articles (1996-2013). To investigate blood lead levels (BLL) and possible risk factors for lead exposure among children in China A total of 388 articles met our inclusion criteria. The overall geometric mean (GM) BLL was 71 μg/L, and the prevalence of elevated BLL (EBLL, defined as BLL &ge;100 μg/L) was 18.48% among children. The prevalence of EBLL remained significantly higher among boys. In children less than 6 years of age, there were significantly increasing trends in both BLL and prevalence of EBLL in an age-dependent manner. The ban on leaded gasoline significantly reduced the BLL as well as EBLL prevalence; however, children whose parents had lower educational levels or were exposed to lead in the workplace had a higher EBLL prevalence. Despite its decline over time, the average BLL among children in China remains higher than the average level most recently reported in the United States. Childhood lead poisoning remains a public health problem in China.

Arsenic in Drinking Water Toxicological Risk Assessment in the North Region of Burkina Faso

Human health risks assessment were estimated by determining the nature and probability of adverse health effects in the North region’s populations who are now exposed to arsenic from drinking water or will be exposed in the future. Several questions were addressed in this study: what types of health problems may be caused by arsenic from drinking water? What is the chance that people will experience health problems when exposed to different levels of arsenic? What arsenic level are people exposed to and for how long? To answers these questions we have first identified the hazard by evaluating arsenic concentration in thirty-four (34) bore-hole water points among the region based on the assumption of clinical cases related to drinking water. Arsenic concentration ranged from 0 up to 87.8 micrograms per liter. Next we assessed the dose-response of exposure to arsenic. Dose-response relationship describes how the likelihood and severity of adverse health effects are related to the amount and condition of exposure to arsenic. This required us to choose toxicity reference values (TRVs) above which adverse effects may occur for noncarcinogenic and for carcinogenic effects. Exposure factors have been calculated in two scenarios: people from 0 to 14 years old and people from 15 to 70 years. Exposure has been estimated indirectly through consideration of measured concentrations of arsenic in drinking water. This study show that people in the Yatenga, Zondoma and Passore provinces are at very high risk for developing several pathologies such as hyper pigmentation, keratosis, cancer, etc. due by chronic exposure to arsenic in drinking water.

Establishment of a Risk Assessment Framework for Analysis of the Spread of Highly Pathogenic Avian Influenza

To evaluate the risk of highly pathogenic avian influenza （HPAI） in China's Mainland, a risk assessment framework was built. Risk factors were determined by analyzing the epidemic data using the brainstorming method; the analytic hierarchy process was designed to weigh risk factors, and the integrated multicriteria analysis was used to evaluate the final result. The completed framework included the risk factor system, data standards for risk factors, weights of risk factors, and integrated assessment methods. This risk assessment framework can be used to quantitatively analyze the outbreak and spread of HPAI in China's Mainland.

A Simplified ArcGIS Approach for Landslides Risk Assessment in the Province of Bergamo

The paper describes a simplified GIS approach, for landslides risk assessment in the Province of Bergamo, developed for a GIS degree thesis at the faculty of Engineering of the University of Bergamo. The subject has been and still is largely studied by many researchers with the aid of rigorous mathematical/statistical analysis tools. This work follows some procedures carried out by other studies, but at the end it has been decided to adopt a simple, fast and not rigorous way to find a solution. A following analysis, on the higher risk areas identified, has also been performed to test their reliability, allowing achieving satisfactory results. It has been planned to study the risk model more thoroughly, by taking into account other triggering causes for landslide susceptibility and to try also a rigorous approach, so as to get a better idea of the results achieved so far and how to improve them.

Fuzzy Risk Assessment Model of Power Transformer Based on Borda Number Theory

The power transformer is the key equipment of transforming voltage and exchanging power in the power system.It's safe and reliable operation directly influences the safe level of the power system.To study the risk assessment of power transformer which is very significant to improve the reliability of the power system,a fuzzy comprehensive risk assessment model of power transformer based on Borda number theory is proposed in this paper.At first,the fault types and risk factors of the power transformer are analyzed.Secondly,the basic framework of the fuzzy comprehensive evaluation is applied to quantify the risk factors.And then,Borda number theory is employed to analyze influence degree and occurrence probability of power transformer.At last,the various risk factors impact index and fuzzy comprehensive evaluation index of power transformer can be easily obtained.Applying this model,the relative importance degree of the risk factors can be horizontally compared according to the numerical index,the engineering staff can directly get the parameters of the transformer risk level and get a good description of the visual expression through using 5 score and similar visual language.

Comprehensive insight into the occurrence characteristics,influencing factors and risk assessments of antibiotics in the Chaohu Basin

The pollution of antibiotics in aquatic environments has received extensive attention.Yet,research on antibiotic contamination in river-lake systems,a significant form of modern aquatic environments,still needs to be explored.This study focuses on the Chaohu Basin(China)investigating the occurrence characteristics,influencing factors,and risk assessments of antibiotics in the river-lake system.The total antibiotic concentrations in the water phase and sediment phase were 3.14–1887.49 ng/L and 0.92–1553.75 ng/g,respectively.Clindamycin was the predominant antibiotic in the water phase,whereas tetracycline prevailed in the sediment phase.Notable differences in concentration and structural composition of antibiotics between the tributaries(river system)and Chaohu Lake were observed,indicating the involvement of various geochemical processes in the attenuation of antibiotics during transport to the receiving lake.Spatial analysis suggested that the western river is the primary source of antibiotics in Chaohu Lake.Controlling nutrient influx in heavily polluted areas is crucial to addressing the escalating issue of antibiotic pollution in the river-lake system.The widespread occurrence of clindamycin in the waters is likely due to wastewater treatment plant discharges,and high-intensity human activities continue to exacerbate antibiotic contamination.Risk assessment indicated that sulfamethoxazole,tetracycline,lincomycin,and clindamycin ranked in the top four with the highest risks to the most sensitive aquatic organisms.Nonetheless,the antibiotics presented no risk to consumer health.This study provides valuable insights for controlling antibiotic pollution in riverlake systems.

Understanding Osteoporosis: Pathophysiology, Risk Factors, Diagnosis, and Management

Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and deterioration of bone architecture, resulting in reduced bone strength and, consequently, increased susceptibility to fractures which poses a significant public health concern worldwide, particularly in aging populations [1]. The health-economic impact of vertebral and hip fractures has been extensively explored and it is well known that these fractures are associated with morbidity/disability and increased mortality;they also account for a substantial portion of the direct fracture costs. This review aims to provide a comprehensive overview of osteoporosis, including its pathophysiology, risk factors, diagnostic approaches, and management strategies. By elucidating the multifaceted nature of this condition, healthcare providers can better identify individuals at risk, implement preventive measures, and optimize treatment to reduce the burden of osteoporotic fractures.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

Diagnostic value of ABCD2 and ABCD3-Ⅰ risk scoring systems in determining one-month risk of stroke in patients with transient ischemic attack: An observational study

Objective:To compare the diagnostic value of the ABCD2 and ABCD3-栺scoring systems in predicting the development of a 30-day neurological event in adult patients with transient ischemic attack(TIA)presenting to the emergency department.Methods:The study was observational and prospective and was conducted in a single center.The diagnostic values of the ABCD2 and ABCD3-栺scores in predicting a neurological event within one month were compared in patients diagnosed with TIA in the emergency department.Results:A statistically significant difference was observed between groups with or without stroke within one month in terms of both the ABCD2 and ABCD3-栺scores(P=0.044 and P=0.029,respectively).There was no statistically significant difference between the patients with and without a recurrent TIA within one month in relation to the ABCD2 score(P=0.934),but a statistically significant difference was found in the ABCD3-栺scores of these groups(P<0.001).Conclusions:Both the ABCD2 and ABCD3-栺scoring systems could predict ischemic stroke within 30 days of TIA,the ABCD3-栺score is more effective than the ABCD2 score in the prediction of TIA recurrence.

Research on Assessment Model of Information System Security Based on Various Security Factors

With the rapid development of network technology, the meaning of layers and attributes in respect of information system security must be extended based on the understanding of the concept of information system security. The layering model (LM) of information system security and the five-attribute model (FAM) based on security factors were put forward to perfect the description and modeling of the information system security framework. An effective framework system of risk calculation and assessment was proposed, which is based on FAM.

Risk Assessment of Unmanned Aerial Vehicle Flight Based on Kmeans Clustering Algorithm

To quantify unmanned aerial vehicle(UAV)flight risks in low-altitude airspace,we analyze the factors of UAV flight risks from three aspects:flight conflict,flight environment,and traffic characteristics.The aerial risk index and ground risk index of the UAV are constructed,the index screening model and the UAV flight risk assessment model are established,and a UAV flight risk assessment model based on K-means clustering has been proposed.Meanwhile,numerical simulations show the proposed method can not only evaluate the UAV flight risks effectively,but also provide technical support for UAV risk management and control.

A Review of Research on Risk Assessment of Fresh Agricultural Product Supply Chain

With the establishment of a well-off society in an all-round way,Chinese residents have put forward higher requirements for the quality and safety of fresh agricultural products.In recent years,domestic and foreign academic circles have also paid more and more attention to the risk of fresh agricultural product supply chain.This article collects and sorts out the research findings of domestic and foreign scholars on the risk assessment of fresh agricultural product supply chain in recent years,and summarizes and discusses from three aspects:supply chain risk factors,risk assessment indicator selection,and risk assessment methods.It is intended to provide a reference for future new research ideas and research perspectives.

Psychological Risk Factors of Terrorist Offenders in Indonesia

Psychological criminogenic factors for identifying terrorist offenders at risk of recidivism in Indonesia remain unclear;hence the adequate assessment to those involved with terrorism and measurement of effective terrorism rehabilitation are questioned.‘MIKRA’Risk Assessment was developed to identify individual criminogenic risk factors and needs of terrorist offenders in Indonesia.It is formulated to set up future parameters of effective terrorism rehabilitation.MIKRA study involved thirty-two eminent Indonesian counterterrorism experts and practitioners in semi-structured interviews and qualitative data analysis.The study identifies 18 individual risk factors and needs of ideology-based terrorist offenders that are grouped into one of three higher order domains:Motivation,Ideology,and Capability.