Evaluation Of Prediction Models Research Articles

A predictive model for medium-term weight loss response in people with type 2 diabetes engaging in behavioural weight management interventions.

To develop and evaluate prediction models for medium-term weight loss response in behavioural weight management programmes. We conducted three longitudinal analyses using the Action for HEalth in Diabetes (LookAHEAD) trial, Weight loss Referrals for Adults in Primary care (WRAP) trial, and routine data from the National Health Service Greater Glasgow and Clyde Weight Management Service (NHS-GGCWMS). We investigated predictors of medium-term weight loss (>5% body weight) over 3 years in NHS-GGCWMS and, separately, predictors of weight loss response in LookAHEAD over 4 years. We validated predictors in both studies using WRAP over 5 years. Predictors of interest included demographic and clinical variables, early weight change in-programme (first 4 weeks) and overall in-programme weight change. In LookAHEAD and WRAP the only baseline variables consistently associated with weight loss response were female sex and older age. Of 1152 participants in NHS-GGCWMS (mean age 57.8 years, 60% female, type 2 diabetes diagnosed for a median of 5.3 years), 139 lost weight over 3 years (12%). The strongest predictor of weight loss response was early weight change (odds ratio 2.22, 95% confidence interval 1.92-2.56) per 1% weight loss. Losing 0.5% weight in the first 4 weeks predicted medium-term weight loss (sensitivity 89.9%, specificity 49.5%, negative predictive value 97.3%). Overall in-programme weight change was also associated with weight loss response over 3 years in NHS-GGCWMS and over 5 years in WRAP. Not attaining a weight loss threshold of 0.5% early in weight management programmes may identify participants who would benefit from alternative interventions.

Weighted metrics are required when evaluating the performance of prediction models in nested case–control studies

BackgroundNested case–control (NCC) designs are efficient for developing and validating prediction models that use expensive or difficult-to-obtain predictors, especially when the outcome is rare. Previous research has focused on how to develop prediction models in this sampling design, but little attention has been given to model validation in this context. We therefore aimed to systematically characterize the key elements for the correct evaluation of the performance of prediction models in NCC data.MethodsWe proposed how to correctly evaluate prediction models in NCC data, by adjusting performance metrics with sampling weights to account for the NCC sampling. We included in this study the C-index, threshold-based metrics, Observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis. We illustrated the proposed metrics with a validation of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in data from the population-based Rotterdam study. We compared the metrics obtained in the full cohort with those obtained in NCC datasets sampled from the Rotterdam study, with and without a matched design.ResultsPerformance metrics without weight adjustment were biased: the unweighted C-index in NCC datasets was 0.61 (0.58–0.63) for the unmatched design, while the C-index in the full cohort and the weighted C-index in the NCC datasets were similar: 0.65 (0.62–0.69) and 0.65 (0.61–0.69), respectively. The unweighted O/E ratio was 18.38 (17.67–19.06) in the NCC datasets, while it was 1.69 (1.42–1.93) in the full cohort and its weighted version in the NCC datasets was 1.68 (1.53–1.84). Similarly, weighted adjustments of threshold-based metrics and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. In the matched design, the bias of the unweighted metrics was larger, but it could also be compensated by the weight adjustment.ConclusionsNested case–control studies are an efficient solution for evaluating the performance of prediction models that use expensive or difficult-to-obtain biomarkers, especially when the outcome is rare, but the performance metrics need to be adjusted to the sampling procedure.

Quality evaluation and shelf-life prediction model establishment of deodorization bovine liver

Quality evaluation and shelf-life prediction model establishment of deodorization bovine liver

A Study of Tennis Score Evaluation Based on Logistic Regression and LSTM Neural Networks

In order to study the performance of athletes in the competition, this paper establishes a comprehensive evaluation mechanism and prediction model through mathematical modeling. Firstly, this paper uses the clustering algorithm to categorize the data and establishes a score evaluation model according to the weights. Then, a momentum evaluation score calculation function was constructed and compared with the results of logistic regression algorithm. Finally, a multi-indicator data system was established to predict the results of the game using the LSTM neural network model. The experimental results show that the model proposed in this paper has a good prediction effect and provides suggestions to the participants.

DATA-DRIVEN ASSESSMENT OF SHEAR STRENGTH OF SINUSOIDAL CORRUGATED STEEL WEBS

The construction industry increasingly recognizes the value of steel beams with corrugated webs for their enhanced resistance to buckling, superior shear capacity, and the elimination of the need for web stiffeners. While a wealth of research focuses on beams with trapezoidal corrugated webs, investigations into sinusoidal corrugated webs (SCWs) remain comparatively sparse. This work investigates the variability in the prediction of the various models and assesses the interactions among different influencing parameters affecting the shear strength of sinusoidal corrugated steel web beams. A comprehensive database of 66 experimental data on SCW beams is assembled and rigorously examined to facilitate this analysis. The collected database is used to test the prediction accuracy of the existing design models provided by codes or proposed models in the literature. The Eurocode3 and DASt-Ri. 015 code models are considered in this study. The results of the evaluated prediction models showed considerable scatters, emphasizing the ongoing need for research to enhance the accuracy of these predictive models for better application in construction practices.

DATA-DRIVEN ASSESSMENT OF SHEAR STRENGTH OF SINUSOIDAL CORRUGATED STEEL WEBS

The construction industry increasingly recognizes the value of steel beams with corrugated webs for their enhanced resistance to buckling, superior shear capacity, and the elimination of the need for web stiffeners. While a wealth of research focuses on beams with trapezoidal corrugated webs, investigations into sinusoidal corrugated webs (SCWs) remain comparatively sparse. This work investigates the variability in the prediction of the various models and assesses the interactions among different influencing parameters affecting the shear strength of sinusoidal corrugated steel web beams. A comprehensive database of 66 experimental data on SCW beams is assembled and rigorously examined to facilitate this analysis. The collected database is used to test the prediction accuracy of the existing design models provided by codes or proposed models in the literature. The Eurocode3 and DASt-Ri. 015 code models are considered in this study. The results of the evaluated prediction models showed considerable scatters, emphasizing the ongoing need for research to enhance the accuracy of these predictive models for better application in construction practices.

Developing a geographical-meteorological indicator system and evaluating prediction models for alveolar echinococcosis in China.

Geographical and meteorological factors have been reported to influence the prevalence of echinococcosis, but there's a lack of indicator system and model. To provide further insight into the impact of geographical and meteorological factors on AE prevalence and establish a theoretical basis for prevention and control. Principal component and regression analysis were used to screen and establish a three-level indicator system. Relative weights were examined to determine the impact of each indicator, and five mathematical models were compared to identify the best predictive model for AE epidemic levels. By analyzing the data downloaded from the China Meteorological Data Service Center and Geospatial Data Cloud, we established the KCBIS, including 50 basic indicators which could be directly obtained online, 15 characteristic indicators which were linear combination of the basic indicators and showed a linear relationship with AE epidemic, and 8 key indicators which were characteristic indicators with a clearer relationships and fewer mixed effects. The relative weight analysis revealed that monthly precipitation, monthly cold days, the difference between negative and positive temperature anomalies, basic air temperature conditions, altitude, the difference between positive and negative atmospheric pressure anomalies, monthy extremely hot days, and monthly fresh breeze days were correlated with the natural logarithm of AE prevalence, with sequential decreases in their relative weights. The multinomial logistic regression model was the best predictor at epidemic levels 1, 3, 5, and 6, whereas the CART model was the best predictor at epidemic levels 2, 4, and 5.

Fuzzy reliability evaluation and machine learning-based fault prediction of wind turbines

The swift growth of the wind power industry necessitates comprehensive evaluation and efficient fault prediction of wind turbines. Given the challenges of integration and optimization of reliability evaluation and fault prediction models, a systematic method of reliability fuzzy evaluation and fault prediction based on the Supervisory Control and Data Acquisition (SCADA) data is proposed. A mid-to-long-term reliability fuzzy evaluation model is constructed using Fuzzy Comprehensive Evaluation (FCE). The mid-term evaluation results in ten failure modes reveal that the model's hazard ranking results match the situation better than the RPN method. And the long-term evaluation results of 5 years in the operating mode show that the model effectively gathers the evaluation information each year and provides a clear and accurate reflection of reliability. Meanwhile, fault prediction is studied using alarm logs because they are better at expressing the status of wind turbines than monitoring data. And the tree-based algorithms and unsupervised statistical learning methods are used to mine the mapping relationship between input variables and predefined tags. The fault prediction achieves both accuracy and recall of 0.784 and saves over 163k Euros based on local wind turbine maintenance expenditures. Overall, the reliability evaluation and fault prediction complement each other, which may either affect future wind farm management or prevent unnecessary maintenance costs.

A homogenized two-phase computational framework for meso- and macroscale blood flow simulations

Background and ObjectiveOwing to the complexity of physics linked with blood flow and its associated phenomena, appropriate modeling of the multi-constituent rheology of blood is of primary importance. To this effect, various kinds of computational fluid dynamic models have been developed, each with merits and limitations. However, when additional physics like thrombosis and embolization is included within the framework of these models, computationally efficient scalable translation becomes very difficult. Therefore, this paper presents a homogenized two-phase blood flow framework with similar characteristics to a single fluid model but retains the flow resolution of a classical two-fluid model. The presented framework is validated against four different sets of experiments. MethodsThe two-phase model of blood presented here is based on the classical diffusion-flux framework. Diffusion flux models are known to be less computationally expensive than two-fluid multiphase models since the numerical implementation resembles single-phase flow models. Diffusion flux models typically use empirical slip velocity correlations to resolve the motion between phases. However, such correlations do not exist for blood. Therefore, a modified slip velocity equation is proposed, derived rigorously from the two-fluid governing equations. An additional drag law for red blood cells (RBCs) as a function of volume fraction is evaluated using a previously published cell-resolved solver. A new hematocrit-dependent expression for lift force on RBCs is proposed. The final governing equations are discretized and solved using the open-source software OpenFOAM. ResultsThe framework is validated against four sets of experiments: (i) flow through a rectangular microchannel to validate RBC velocity profiles against experimental measurements and compare computed hematocrit distributions against previously reported simulation results (ii) flow through a sudden expansion microchannel for comparing experimentally obtained contours of hematocrit distributions and normalized cell-free region length obtained at different flowrates and inlet hematocrits, (iii) flow through two hyperbolic channels to evaluate model predictions of cell-free layer thickness, and (iv) flow through a microchannel that mimics crevices of a left ventricular assist device to predict hematocrit distributions observed experimentally. The simulation results exhibit good agreement with the results of all four experiments. ConclusionThe computational framework presented in this paper has the advantage of resolving the multiscale physics of blood flow while still leveraging numerical techniques used for solving single-phase flows. Therefore, it becomes an excellent candidate for addressing more complicated problems related to blood flow, such as modeling mechanical entrapment of RBCs within blood clots, predicting thrombus composition, and visualizing clot embolization.

Analysis of factors influencing the efficacy of vagus nerve stimulation for the treatment of drug-resistant epilepsy in children and prediction model for efficacy evaluation.

Vagus nerve stimulation (VNS) has been widely used in the treatment of drug-resistant epilepsy (DRE) in children. We aimed to explore the efficacy and safety of VNS, focusing on factors that can influence the efficacy of VNS, and construct a prediction model for the efficacy of VNS in the treatment of DRE children. Retrospectively analyzed 45 DRE children who underwent VNS at Qilu Hospital of Shandong University from June 2016 to November 2022. A ≥50% reduction in seizure frequency was defined as responder, logistic regression analyses were performed to analyze factors affecting the efficacy of VNS, and a predictive model was constructed. The predictive model was evaluated by receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA). A total of 45 DRE children were included in this study, and the frequency of seizures was significantly reduced after VNS treatment, with 25 responders (55.6%), of whom 6 (13.3%) achieved seizure freedom. There was a significant improvement in the Quality of Life in Childhood Epilepsy Questionnaire (15.5%) and Seizure Severity Score (46.2%). 16 potential factors affecting the efficacy of VNS were included, and three statistically significant positive predictors were ultimately screened: shorter seizure duration, focal seizure, and absence of intellectual disability. We developed a nomogram for predicting the efficacy of VNS in the treatment of DRE children. The ROC curve confirmed that the predictive model has good diagnostic performance (AUC = 0.864, P < 0.05), and the nomogram can be further validated by bootstrapping for 1,000 repetitions, with a C-index of 0.837. Besides, this model showed good fitting and calibration and positive net benefits in decision curve analysis. VNS is a safe and effective treatment for DRE children. We developed a predictive nomogram for the efficacy of VNS, which provides a basis for more accurate selection of VNS patients.

A Case Study for Stability Analysis of a Toppling Bank Slope with Fault Fracture Zones Developed under the Action of Bridge Loads and Reservoir Water

The mountainous areas of Southwest China have the characteristics of valley deep-cutting, a large topographic gradient, complex geological structures, etc. With the development of infrastructure construction in the area, the construction of bridges across valleys has gradually increased, and the phenomenon of slope failure occurs more and more frequently. As the weak interlayer, the fault fracture zones have a significant influence on the geological structure and stability of slopes, while the complexity of the mechanism of the deformation and failure of slopes increases with the combination of the development of the fracture zones and toppling deformation. This paper took the toppling bank slope of bridge foundations developed with fault fracture zones in Lancang River as the research object. Through an on-site field survey and geological survey technologies, it identified the distribution range of the fracture zones on the bank slope and determined the characteristics of the rock mass in the fracture zones. A stability evaluation model for the bank slope of the bridge foundations was established using the limit equilibrium method and discrete element method. Based on the two-dimensional limit equilibrium analysis, the potential failure modes of the bank slope were explored, and the stability of the bank slope under bridge loads was calculated. Through the three-dimensional geological model of the bank slope, including the fracture zones and toppling bodies, the three-dimensional discrete element numerical simulation method was adopted to simulate and calculate the deformation and failure process of the bank slope under different bridge loads and working conditions. According to the calculation results, the influence of bridge loads and reservoir water on the stability of the bank slope was analyzed from the perspectives of displacement, plastic zone, stability coefficient, and other factors. The formation process of the plastic zone and the development of the sliding surface were revealed, the incentive mechanism of bridge loads and reservoir water on the deformation and failure of the bank slope was analyzed, and the influence of fault fracture zones on the stability of the bank slope and the development of toppling deformation was determined. The results indicate that the fault fracture zones are important geological structures that affect the deformation and failure of the bank slope as a weak interlayer. Under the influence of bridge loads and reservoir water, the stability of the bank slope is affected by the quality of the rock mass and the development of the fault fracture zones, resulting in the unmet need for safety requirements and maybe leading to instability. Based on the calculation results of the stability evaluation prediction model for the bridge foundation bank slope and the engineering geological conditions, the bridge scheme has been selected.

A Risk Prediction Model for Adverse Events after Surgical Valve Replacement

Background. Although several risk-predictive models for patients undergoing surgical valve replacement (SVR) have been published, reports on composite endpoints of adverse events in these patients are limited. This study aimed to establish a novel, easy-to-use prognostic prediction model of composite endpoints in patients following SVR. Methods. According to the inclusion criteria, patients with successful SVR were enrolled. Adverse events, including heart failure hospitalization, stroke, major bleeding, uncontrolled infection, secondary surgery, postoperative arrhythmia, and all-cause mortality during follow-up, were tracked. All datasets were randomly divided into the derivation and validation cohorts at a ratio of 7 to 3. Logistic regression analysis was used to screen for independent predictors and construct a nomogram for adverse events. We further presented a calibration curve and decision curve analysis for evaluating prediction models. Results. According to the multivariate logistic regression analyses, three variables were selected for the final predictive model, including platelet-to-lymphocyte ratio, diabetes mellitus, and albumin. A nomogram was then constructed to present the results. The C-index of the model was 0.73 (95% confidence interval: 0.65–0.81) for the derivation cohort and 0.75 (95% confidence interval: 0.64–0.86) for the validation cohort. The calibration curve demonstrated that the results of the nomogram agreed with actual observations (Brier score = 0.09). Conclusions. We developed an effective nomogram to predict the occurrence of composite adverse events in patients following SVR. This model could be used to evaluate the mid-term risks of adverse events as well as provide clinicians and patients with a basis for decision-making.

Investigation on evaluation and prediction of surface roughness and terahertz reflectivity analysis of the SiCp/Al composites component by diamond turning

SiCp/Al composites are suitable for satisfying the lightweight requirements of terahertz wave-reflecting components. In this paper, diamond-turned surfaces of 60 % high volume fraction SiCp/Al composites are characterized in terms of surface roughness and terahertz reflectivity. The finished surfaces of SiCp/Al composites are obtained using single-point diamond turning with different process parameters, and surface roughness measurements as well as SEM and EDS analyses are conducted on the surfaces. The theoretical relationship between surface roughness and terahertz reflectivity is analyzed, and terahertz reflectivity testing experiments are conducted and validated. The surface roughness evaluation system and prediction model for SiCp/Al composites with a volume fraction of 60 % are established, and the errors between the experimental results and the predicted results are verified within 10 % by random experiments. Finally, the turning process parameters are optimized using a genetic algorithm, and the optimized turning process parameters are obtained as the spindle speed of 750 r/min, the feeding speed of 1 mm/min and the turning depth of 6 μm.

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires. Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors. Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models. The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.

Predictors of micronutrient deficiency among children aged 6-23 months in Ethiopia: a machine learning approach.

Micronutrient (MN) deficiencies are a major public health problem in developing countries including Ethiopia, leading to childhood morbidity and mortality. Effective implementation of programs aimed at reducing MN deficiencies requires an understanding of the important drivers of suboptimal MN intake. Therefore, this study aimed to identify important predictors of MN deficiency among children aged 6-23 months in Ethiopia using machine learning algorithms. This study employed data from the 2019 Ethiopia Mini Demographic and Health Survey (2019 EMDHS) and included a sample of 1,455 children aged 6-23 months for analysis. Machine Learning (ML) methods including, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Naïve Bayes (NB) were used to prioritize risk factors for MN deficiency prediction. Performance metrics including accuracy, sensitivity, specificity, and Area Under the Receiver Operating Characteristic (AUROC) curves were used to evaluate model prediction performance. The prediction performance of the RF model was the best performing ML model in predicting child MN deficiency, with an AUROC of 80.01% and accuracy of 72.41% in the test data. The RF algorithm identified the eastern region of Ethiopia, poorest wealth index, no maternal education, lack of media exposure, home delivery, and younger child age as the top prioritized risk factors in their order of importance for MN deficiency prediction. The RF algorithm outperformed other ML algorithms in predicting child MN deficiency in Ethiopia. Based on the findings of this study, improving women's education, increasing exposure to mass media, introducing MN-rich foods in early childhood, enhancing access to health services, and targeted intervention in the eastern region are strongly recommended to significantly reduce child MN deficiency.

Research on Olympic Games Hosting Scheme Based on Comprehensive Evaluation and Grey Prediction Model

Owing to the confluence of financial, environmental, and societal pressures confronting both the host city and nation, the allure of bidding for the Olympic Games across diverse global regions is experiencing a discernible wane. In an effort to proffer a structured framework for Olympic Games bidding, this paper undertakes an initial phase of indicator selection and subsequent determination of their respective weights. We have meticulously curated a selection of the utmost fitting cities to serve as potential hosts for the grandeur of the Summer Olympic Games across the global stage. This intricate process is achieved through the confluence of methodologies including the Delphi Method, the Analytic Hierarchy Process (AHP), and the EWM-TOPSIS Method. Subsequently, guided by the Comprehensive Evaluation Model, the study identifies a pair of fitting global locales poised for the enduring stewardship of the summer and winter Olympic Games. This paper culminates in scrutinizing the feasibility of the proposed scheme. This scrutiny takes the form of an inquiry into both immediate and prolonged repercussions of the Olympic Games upon the host city or nation. Parameters of Real GDP per capita, the proportion of international trade taxes, and carbon emissions are dissected, employing the discerning capabilities of the GREY Model. The outcomes of this inquiry distinctly underscore the efficacy of the strategy involving a fixed hosting address, bolstering affirmative developmental trajectories for the host locale. This study bears substantial reference value for the Olympic Organizing Committee in its pursuit of identifying a fresh and distinctive Olympic venue.

Retracted: A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area.

[This retracts the article DOI: 10.1155/2022/1805689.].

Risk factors' prediction model for the investment evaluation

Risk factors' prediction model for the investment evaluation

Predictive value of lung function measures for cardiovascular risk: a large prospective cohort study

IntroductionAlthough lung function measures are associated with cardiovascular disease (CVD), the added predictive values of these measures remain unclear.MethodsFrom the UK Biobank, 308 415 participants free of CVD with spirometry...

A model for predicting return of spontaneous circulation and neurological outcomes in adults after in-hospital cardiac arrest: development and evaluation.

In-hospital CA (IHCA) is associated with rates of high incidence, low return of spontaneous circulation (ROSC), low survival to discharge, and poor neurological outcomes. We aimed to construct and evaluate prediction models for non-return of spontaneous circulation (non-ROSC) and poor neurological outcomes 12 months after ROSC (PNO-12). We retrospectively analyzed baseline and clinical data from patients experiencing cardiac arrest (CA) in a big academic hospital of Jilin University in China. Patients experiencing CA between September 1, 2019 and December 31, 2020 were categorized into the ROSC and non-ROSC groups. Patients maintaining ROSC >20 min were divided into the good and PNO-12 subgroups. Univariate and multivariate logistic regression identified independent factors associated with non-ROSC and PNO-12. Two nomogram prediction models were constructed and evaluated. Of 2,129 patients with IHCA, 851 were included in the study. Multivariate logistic regression analysis revealed that male sex, age >80 years, CPR duration >23 min, and total dose of adrenaline >3 mg were significant risk factors for non-ROSC. Before CA, combined arrhythmia, initial defibrillation rhythm, and advanced airway management (mainly as endotracheal intubation) also influenced outcomes. The area under the receiver operating characteristic curve in the prediction model was 0.904 (C-index: 0.901). Respiratory failure, shock, CA in the monitoring area, advanced airway management, and noradrenaline administration were independent risk factors for PNO-12. The AUC was 0.912 (C-index: 0.918). Prediction models based on IHCA data could be helpful to reduce mortality rates and improve prognosis.