Prediction Performance Of Model Research Articles

Lithium-Ion Battery State of Health Estimation Based on Model Interpretable Feature Extraction

Abstract This study proposes a lithium battery State of Health (SOH) estimation method that utilizes model interpretability feature extraction and the Equilibrium Optimizer (EO) algorithm to optimize Temporal convolutional neural networks (TCN), addressing issues of feature collinearity, noise interference, and the challenges of manual model parameter tuning. Initially, the battery's incremental capacity (IC) curve is smoothed using Gaussian filtering, and the health features are extracted from the charging, discharging and IC curve to establish a TCN-based SOH estimation model. Subsequently, the SHAP interpretability method is employed to analyze the contribution of various features to the TCN model's predictions, and the features were further screened based on these contributions; the EO algorithm is used to optimize the TCN model hyperparameters, enhancing the model's prediction performance. Finally, this study builds an experimental platform for ageing tests to validate this method with experimental data and public datasets. The results show that SHAP analysis and the EO algorithm, based on the model's real-time feedback mechanism, significantly improved the accuracy of feature selection and model prediction precision. The method proposed in this study achieves an RMSE of below 1.40% for the SOH prediction of six batteries, reducing it by 66.7% compared to the baseline model.

Prediction of Pellet Durability Index (PDI) in a Commercial Feed Mill Using Multiple Linear Regression with Variable Selection and Dimensionality Reduction.

Pellet quality, measured as Pellet Durability Index (PDI), is an important key performance indicator (KPI) for commercial feed manufacturing, as it can impact both mill efficiency and downstream performance of animals fed the manufactured diets. However, it is an ongoing challenge for the feed industry to control pellet quality, due to the complexity of feed manufacturing and the large number of variables influencing the process. Previous studies have explored prediction of pellet quality using either simple empirical models with a few variables or machine learning models with many variables. The objective of the current study was to develop statistical regression models to predict PDI, and to describe the relationship between pellet quality and 55 available variables based on a dataset with 2691 observations collected from a commercial feed mill. In the current study, the response variable (PDI) was transformed using the Box-Cox approach into the transformed response variable (tPDI), that was more normally distributed. Three multiple regression models were developed based on subsets of variables processed by variable selection and dimensionality reduction methods: Forward Selection, Principal Component Analysis, and Partial Least Squares. The results indicated that Model 1 (Forward Selection with manual removal of sparse variables), built on 9 variables, performed better than the other two models. It exhibited consistent model prediction performance on the training data and testing data, in terms of MAE (1.93 ± 0.063 versus 1.96), RMSPE (2.45 ± 0.079 versus 2.45), and CCC (0.549 ± 0.0273 versus 0.550), with a better prediction precision based on the fit plot. Expanding Temperature (℃), Fat Content (%), and ADF Content (%), and Indoor Humidity (Pelletizer) (%) were identified as more influential than other variables on the transformed response variable (tPDI) in Model 1, based on a behavior analysis. The models developed in the current study can be helpful to feed mills for predicting and comprehending the effect of a number of commonly measured variables on pellet quality in the commercial setting.

Advancing Blast Furnace Thermal State Prediction: A Data‐Driven Approach Using Thermocouple Integration and Multimodal Modeling

This study develops a data‐driven framework to predict the thermal state of blast furnaces using feature fusion from thermocouple data and spatial temperature distribution. The article proposes a hybrid framework based on multimodal integration and clustering algorithms, utilizing data extracted from thermocouples and the temperature distribution features around the furnace hearth. Through these fused features, multiple ensemble models are constructed to predict the thermal state of the blast furnace, with a focus on the thermocouple readings at the hearth. This method enhances understanding of the thermal state of the blast furnace, aiming to improve prediction accuracy and operational reliability. By validating the model with actual industrial data, its effectiveness in thermal state monitoring is demonstrated. The integration of multimodal data sources allows for the extraction of rich information from the thermocouple data, significantly enhancing the model's predictive performance.

A quantitative evaluation of the prediction performance of a one-dimensional multifluid population balance model in continuous and semi-batch bubble columns

A quantitative evaluation of the prediction performance of a one-dimensional multifluid population balance model in continuous and semi-batch bubble columns

Impact analysis of uncertainty in thermal resistor-capacitor models on model predictive control performance

Model Predictive Control (MPC) is extensively utilized for optimal control in building systems. Despite substantial research being dedicated to exploring the impact of uncertainties in external and internal disturbances on the performance of MPC, the existing studies neglect the potential impact of uncertainties in model parameter identification on control performance. To address this gap, this study quantifies the impact of model uncertainty on MPC performance through a test case in a virtual environment. Various levels of uncertainties for parameters R and C are artificially introduced to assess the MPC performance. The causes of the impact of model uncertainty on control performance are further explored through analysis. We select a first-order RC model to modelling building thermal dynamics. MPC is employed to optimize the heat pump signal with the goal of minimizing the energy cost while maintaining thermal comfort. The simulation results demonstrate that a negative deviation in model parameter identification has a more pronounced impact on MPC performance than a positive deviation, which has a negligible effect on MPC control performance. Deviations in parameters from their true values affect both heat losses from the zone and thermal capacity, thereby influencing the estimated temperature by the RC model. Consequently, these factors, in turn, affect the system’s control decisions, leading to variations in the objective function values. This study can provide an insight into the relationship of model parameters uncertainties and MPC performance and facilitate the practical application of MPC in buildings.

Fairness in Algorithmic Profiling: The AMAS Case

We study a controversial application of algorithmic profiling in the public sector, the Austrian AMAS system. AMAS was supposed to help caseworkers at the Public Employment Service (PES) Austria to allocate support measures to job seekers based on their predicted chance of (re-)integration into the labor market. Shortly after its release, AMAS was criticized for its apparent unequal treatment of job seekers based on gender and citizenship. We systematically investigate the AMAS model using a novel real-world dataset of young job seekers from Vienna, which allows us to provide the first empirical evaluation of the AMAS model with a focus on fairness measures. We further apply bias mitigation strategies to study their effectiveness in our real-world setting. Our findings indicate that the prediction performance of the AMAS model is insufficient for use in practice, as more than 30% of job seekers would be misclassified in our use case. Further, our results confirm that the original model is biased with respect to gender as it tends to (incorrectly) assign women to the group with high chances of re-employment, which is not prioritized in the PES’ allocation of support measures. However, most bias mitigation strategies were able to improve fairness without compromising performance and thus may form an important building block in revising profiling schemes in the present context.

Sound absorption performance prediction of multi-dimensional Helmholtz resonators based on deep learning and hyperparameter optimization

Abstract The problem of low-frequency noise is becoming increasingly severe and measuring the sound absorption performance of acoustic metamaterials (AMs) using accurate absorption coefficients is of great interest in low-frequency noise control engineering. Conventional calculation methods such as Finite Element Method (FEM) simulations and the theoretical analysis methods (TAM) have specific limitations. Deep learning (DL) models provide new perspectives for studying AMs acoustic performance. However, the prediction performance of DL models is highly dependent on the proper tuning of hyperparameters. As far as is known, existing literature has not systematically explored the impact of hyperparameter tuning on DL models in the context of acoustic performance studies. The present paper designed a multi-dimensional Helmholtz resonator (MDHR) consisting of a 4 × 4-type continuous parallel arrangement, while a dataset was established via FEM. Furthermore, a deep neural network (HPO-DNNs) model based on hyperparameter optimization (HPO) was proposed to predict the acoustic performance of the MDHR. Random search (RS), Bayesian optimization (BO), Simulated annealing (SA), and genetic algorithm (GA) were introduced to optimize the hyperparameters (learning rate, weight decay, optimizer, and batch size) of the DNNs. The mean square error (MSE), coefficient of determination (R 2) of the testing dataset and the optimization time were used as the evaluation metrics, GA was selected for further study based on the comparison results (MSE = 0.00177, R 2 = 0.98151) of the optimization efficiency and predictive precision of DNNs from the four HPO algorithms. Finally, the prediction performance of the GA-DNNs model was evaluated in single-, multi-, and broadband conditions in practical applications, demonstrating high precision and stability and providing a new approach for acoustics performance studies.

Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy

The size distribution of rock fragments significantly influences subsequent operations in geotechnical and mining engineering projects. Thus, accurate prediction of this distribution according to the relevant blasting design parameters is essential. This study employs artificial intelligence methods to predict the fragmentation of open-pit bench blasting. The study employed a dataset comprising 97 blast fragment samples. Random forest and XGBoost models were utilized as base learners. A prediction model was developed using the stacking integrated strategy to enhance predictive performance. The model’s performance was evaluated using the coefficient of determination (R2), the mean square error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The results indicated that the model achieved the highest prediction accuracy, with an R2 of 0.943. In the training set, the model achieved MSE, RMSE, and MAE values of 0.00269, 0.05187, and 0.03320, while in the testing set, these values were 0.00197, 0.04435, and 0.03687, respectively. The model was validated using five sets of actual blasting block data from a northeastern mining area, which yielded more accurate prediction results. These findings demonstrate that the stacking strategy effectively enhances the prediction performance of a single model and offers innovative approaches to predicting blasting block size.

Statistical models versus machine learning approach for competing risks in proctological surgery.

Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for prediction and classification problems. They can detect non-linear relationships between independent and dependent variables and incorporate many of them. In our work, we aimed to investigate the potential role of machine learning versus classical logistic regression for the preoperative risk assessment in proctological surgery. We used clinical data from a nationwide audit: the database consisted of 1510 patients affected by Goligher's grade III hemorrhoidal disease who underwent elective surgery. We collected anthropometric, clinical, and surgical data and we considered ten predictors to evaluate model-predictive performance. The clinical outcome was the complication rate evaluated at 30-day follow-up. Logistic regression and three machine learning techniques (Decision Tree, Support Vector Machine, Extreme Gradient Boosting) were compared in terms of area under the curve, balanced accuracy, sensitivity, and specificity. In our setting, machine learning and logistic regression models reached an equivalent predictive performance. Regarding the relative importance of the input features, all models agreed in identifying the most important factor. Combining and comparing statistical analysis and machine learning approaches in clinical field should be a common ambition, focused on improving and expanding interdisciplinary cooperation.

Health Insurance Premium Prediction Based on Multiple Machine Learning Algorithms

The purpose of this paper is to predict the health insurance premium through a variety of machine learning algorithms, and compare and analyze the prediction effect of different algorithms. An open source dataset was selected for the study, and the experiments involved three machine learning models: linear regression, decision trees, and random forests. By testing these models, we obtain their performance in health insurance premium forecasts. The results show that the prediction performance of the random forest regression model is better than other models, and its score reaches 0.8564, which is the best algorithm among the three models. Second, the linear regression model has a score of 0.7584, and although its performance is not as good as that of random forest, it still shows some predictive power. Finally, the prediction effect of decision tree model is relatively poor, and the score is only 0.7097. To sum up, the experiments in this paper prove that the random forest model is undoubtedly the best choice in the prediction of health insurance premiums, which not only has good prediction accuracy, but also shows strong data processing ability. In the context of the growing importance of health insurance premium collection and analysis, the use of advanced machine learning algorithms such as Random Forest for forecasting will, to some extent, help insurance companies better price and assess risk. Therefore, it can be concluded that random forest regression model has the best performance for health insurance premium prediction and is an effective tool to achieve accurate prediction.

Nomogram prediction of overall survival in breast cancer patients post-surgery: integrating SEER database and multi-center evidence from China

PurposeOverall survival (OS) in postoperative breast cancer patients is influenced by various clinicopathological features. Current prognostic methods, such as the 7th edition of AJCC staging, have limitations. This study aims to construct and validate a comprehensive nomogram integrating multiple clinicopathological features to predict OS more accurately in breast cancer patients.MethodsWe identified 60,445 .female patients who underwent breast cancer surgery between January 1, 2011, and December 31, 2015, from the Surveillance, Epidemiology, and End Results (SEER) database, randomly divided into training and internal validation cohorts. Additionally, data from 332 breast cancer surgery patients from four hospitals in Taizhou, Zhejiang Province, were included as an external validation cohort. Kaplan-Meier analysis assessed the impact of clinicopathological features on OS, and multivariable Cox regression identified independent prognostic factors. A nomogram based on these factors was constructed to predict 1-, 3-, and 5-year OS. Model predictive performance was evaluated using C-index, AUC, calibration curves, and decision curves during internal and external validation.ResultsMultivariable Cox regression analysis identified age, pathological grade, AJCC stage, ER status, PR status, and HER2 status as independent prognostic factors used in the nomogram construction. The nomogram achieved a C-index of 0.724 (95% CI, 0.716-0.732) in the training cohorts, 0.717 (95% CI, 0.705-0.729) in the internal validation cohorts, and 0.793 (95% CI, 0.724-0.862) in the external validation cohorts, indicating strong discriminative ability. Calibration curves demonstrated good agreement between predicted and observed outcomes in all validation cohorts. Decision curve analysis showed that the nomogram provided maximum net benefit across all validation cohorts.ConclusionThe nomogram developed in this study integrates multiple clinicopathological features and provides a convenient and accurate tool for predicting individualized OS in breast cancer patients. This tool can optimize treatment strategies and improve patient prognosis.

Perfusion image-aided treatment decision for acute ischemic stroke: validation of a clinical decision support system.

Our collaborative team has previously developed a prognostic model for acute ischemic stroke (AIS). This model, known as the clinical decision support system (CDSS), aims to provide personalized assistance to clinicians in making treatment decisions and improving patient prognosis. The objective of this study was to externally validate the model using Chinese AIS patients. All enrolled patients arrived at the hospital within 24 hours after stroke onset. The primary outcome was the likelihood of a favorable functional outcome, which was defined as a modified Rankin Scale (mRS) < 2 at 90 days. The model's predictive performance was evaluated by assessing its discriminative power (area under the curve, AUC) and calibration power (Hosmer-Lemeshow goodness-of-fit test, Brier score). In the validation cohort of 298 patients, the model demonstrated a moderate discriminatory ability to predict a favorable functional outcome (mRS 0-1), with an AUC of 0.805 (95% CI, 0.756-0.849). The calibration performance of the model was assessed using the Hosmer-Lemeshow chi-squared test, yielding a value of 9.211 and a p-value of 0.325 additionally, the Brier score for the prediction of a good outcome was 0.153, further supporting the model's good calibration performance. The study introduces the CDSS that integrates clinical baseline data and imaging indicators of brain perfusion status. This CDSS provides clinicians with an intuitive risk assessment of different treatment strategies for AIS patients. Moreover, the CDSS highlights substantial variations in treatment outcomes among patients, suggesting that it has the potential to significantly enhance personalized treatment approaches.

Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer.

Nasopharyngeal carcinoma (NPC) is a rare disease in most parts of the world, but it is highly prevalent in South China. Epstein-Barr virus (EBV) is one of the major risk factors for NPC. Hence, understanding the factors associated with the reactivation of EBV from the latent stage is crucial for preventing NPC. This study aimed to investigate the risk factors for EBV reactivation associated with NPC in high-prevalence areas in China using a Bayesian network (BN) model combined with structural equation modeling tools. The baseline information for this study was derived from NPC screening data from a population-based prospective cohort in Sihui City, Guangdong Province, China. We divided the data into a training dataset and a test dataset. We then constructed an interaction networktionba BN prediction model to explore the risk factors for EBV reactivation, which was compared with a conventional logistic regression model. A total of 12,579 participants were included in the analyses, with 1596 participant pairs finally included after the use of a nested case-control study. The results of multivariable logistic regression showed that only being older than 60 years (OR = 1.718, 95% CI = 1.273,2.322) and being a current smoker (OR = 1.477, 95% CI = 1.167 - 1.872) were the risk factors for EBV reactivation. The results of the model constructed using BN showed that age and smoking were directly associated with EBV reactivation. In contrast, sex, education level, tea drinking, cooking, and family history of cancer were indirectly associated with EBV reactivation. Further, we predicted the risk of EBV reactivation using Bayesian inference and visualized the BN inference. Model prediction performance was evaluated using the test dataset. The results showed that the BN model slightly outperformed the traditional logistic regression model in all metrics. BN not only reflects the complex interaction between factors but also visualizes the prediction results. It has a promising application potential in the risk prediction of EBV reactivation associated with NPC.

Precision Imaging for Early Detection of Esophageal Cancer.

Early detection of early-stage esophageal cancer (ECA) is crucial for timely intervention and improved treatment outcomes. Hyperspectral imaging (HSI) and artificial intelligence (AI) technologies offer promising avenues for enhancing diagnostic accuracy in this context. This study utilized a dataset comprising 3984 white light images (WLIs) and 3666 narrow-band images (NBIs). We employed the Yolov5 model, a state-of-the-art object detection algorithm, to predict early ECA based on the provided images. The dataset was divided into two subsets: RGB-WLIs and NBIs, and four distinct models were trained using these datasets. The experimental results revealed that the prediction performance of the training model was notably enhanced when using HSI compared to general NBI training. The HSI training model demonstrated an 8% improvement in accuracy, along with a 5-8% enhancement in precision and recall measures. Notably, the model trained with WLIs exhibited the most significant improvement. Integration of HSI with AI technologies improves the prediction performance for early ECA detection. This study underscores the potential of deep learning identification models to aid in medical detection research. Integrating these models with endoscopic diagnostic systems in healthcare settings could offer faster and more accurate results, thereby improving overall detection performance.

Predictors of new persistent opioid use after surgery in adults

PurposePersistent opioid use is one of the most common post-operative complications. Identification of at-risk patients pre-operatively is key to reducing post-operative opioid use. We sought to develop a predictive model for persistent post-operative opioid used and to determine if geographic factors from community databases improve model prediction based solely on electronic health records (EHRs) and claims data.MethodsEHR and claims data for 4,116 opioid-naïve surgical patients older than 18 in North Carolina were linked with census tract-level unemployment data from the American Community Survey and Centers for Disease Control and Prevention data on opioid prescriptions and deaths attributed to drug poisoning. Primary outcome was new persistent opioid use and covariates included patient factors from EHR, claims data, and geographic factors. Multivariable logistic regression models of potential risk factors were evaluated.Results6.0% of patients developed new persistent opioid use. Associated risk factors based on multivariable logistic regressions include age (adjusted odds ratio [AOR] 1.08; 95% confidence interval [CI] 1.00, 1.16), back and neck pain (1.82; 1.39, 2.39), joint disorders (1.58; 1.18, 2.11), mood disorders (1.71; 1.28, 2.28), opioid retail prescription (1.04; 1.00, 1.07) and drug poisoning rates (1.33; 1.09, 1.62). On Monte-Carlo cross-validation, the addition of geographic factors to EHRs and claims may modestly improve prediction performance (area under the curve, AUC) of logistic regression models compared to those based on EHRs and claims data (AUC 0.667 (95% CI 0.619, 0.717) vs AUC 0.653 (0.600, 0.706)).ConclusionsCo-morbidities and area-based factors are predictive of new persistent post-operative opioid use. As the addition of geographic-based factors did not significantly improve performance of multivariable logistic regression, larger samples are needed to fully differentiate models.

Effect of Molecular Structure on the B3LYP-Computed HOMO-LUMO Gap: A Structure -Property Relationship Using Atomic Signatures.

Compounds possessing a small highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap (E gap) are highly desirable due to their instability and reactivity, making them useful for a wide range of applications. However, the search for new organic compounds with a low E gap is an expensive endeavor due to the exponentially increasing pool of virtual compounds. Accordingly, in this study, atomic Signatures were utilized as molecular descriptors to investigate the correlation between the molecular structure and the B3LYP-computed E gap, thus aiding in the development of a quantitative structure-property relationship (QSPR). An easy-to-use robust model was constructed using forward-stepping multilinear regression with leave-one-out cross validation, resulting in a regression coefficient (r 2) of 0.86 and a predictability (q 2) of 0.76. The use of atomic Signatures as molecular descriptors successfully inferred correlations between different structural motifs and E gap. The atomic fragments containing π-bonds in various aromatic compounds were found to be the most significant atomic Signatures, explaining nearly 50% of the variance in the data, with regression coefficients that decreased E gap. This is attributed to π-electron delocalization, making this molecular fragment a reactive site in a molecule. Finally, an external test set was used to further evaluate the model's predictive performance. The developed QSPR can be utilized as a reliable initial screening tool to identify potential candidates possessing low E gap values.

Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm

Achieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it is important for optimizing field management measures and improving crop productivity. To accurately and quickly predict citrus yield, this study obtained multispectral images of citrus fruit maturity through an unmanned aerial vehicle (UAV) and extracted multispectral vegetation indices (VIs) and texture features (T) from the images as feature variables. Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), and multiple stepwise regression (MSR) models were used to construct citrus fruit number and quality prediction models. The results show that, for fruit number prediction, the XGB model performed best under the combined input of VIs and T, with an R2 = 0.792 and an RMSE = 462 fruits. However, for fruit quality prediction, the RF model performed best when only the VIs were used, with an R2 = 0.787 and an RMSE = 20.0 kg. Although the model accuracy was acceptable, the number of input feature variables used was large. To further improve the model prediction performance, we explored a method that utilizes a hybrid coding particle swarm optimization algorithm (CPSO) coupled with XGB and SVM models. The coupled models had a significant improvement in predicting the number and quality of citrus fruits, especially the model of CPSO coupled with XGB (CPSO-XGB). The CPSO-XGB model had fewer input features and higher accuracy, with an R2 &gt; 0.85. Finally, the Shapley additive explanations (SHAP) method was used to reveal the importance of the normalized difference chlorophyll index (NDCI) and the red band mean feature (MEA_R) when constructing the prediction model. The results of this study provide an application reference and a theoretical basis for the research on UAV remote sensing in relation to citrus yield.

Intelligent Optimization Design Framework for Alternating Current Pulse Modulation Electrohydrodynamic Printing Process Parameters.

To achieve efficient size tuning of printed microstructures on insulating substrates, an integrated process parameter intelligent optimization design framework for alternating current pulse modulation electrohydrodynamic (AC-EHD) printing is proposed for the first time. The framework is comprised of two stages: the construction of a prediction model and the acquisition of process parameters. The first stage employs the elk herd optimizer(EHO)-artificial neural network(ANN) to establish a mapping relationship between printing process parameters and the size of deposited droplets. The analysis of the prediction performance of the EHO-ANN model across various datasets reveals that the model exhibits commendable accuracy and robustness in predicting printed droplet size. In the second stage, the process parameters of AC-EHD printing are intelligently determined by utilizing the error between the model output and the desired droplet size as the fitness value for EHO. By comparing three sets of experimental cases with varying droplet sizes, it is observed that the actual printed droplet sizes closely align with the desired values, thus validating the effectiveness of this framework. The framework proposed in this paper mitigates the time and material wastage caused by adjusting AC-EHD printing process parameters on insulating substrates, thereby significantly enhancing the usability of the technology.

Explainable machine learning model for load-carrying capacity prediction of FRP-confined corroded RC columns

This paper proposed a novel explainable machine learning (ML) model to predict the axial load-carrying capacity (Pmax) of FRP-confined corroded RC columns utilizing the eXtreme Gradient Boosting (XGBoost) algorithm and Shapley Additive exPlanations (SHAP) technique. The XGBoost predictive model was constructed based on thorough database of experimental tests for 285 FRP-confined corroded RC columns collected from existing studies and those performed by the authors. Twenty parameters were taken into account as critical input variables to develop the predictive model. SHAP technique was employed for performing the importance evaluation and interpreting the prediction performance of XGBoost model. Additionally, feasibility and effectiveness of the constructed XGBoost model were assessed by using several empirical design models and some other ensemble ML algorithms. The results indicated that, (i) the suggested XGBoost model was validated to be feasible to predict Pmax of FRP-confined corroded RC columns; (ii) the SHAP technique provided good explainability and interpretability to the XGBoost predictive model; (iii) the input variables could be comprehensively studied concerning the feature importance through SHAP technique, and the most important ones affecting the determination of Pmax of FRP-confined corroded RC columns were the gross sectional area of column, FRP thickness, elastic modulus of FRP, eccentricity ratio, corrosion rate, and concrete compressive strength; (iv) the prediction effectiveness and feasibility of the proposed XGBoost model were significantly superior to those of the existing empirical models and other ML algorithms, and the mean values of R2, RMSE, MAE, and MAPE of the XGBoost model were 0.978, 122 kN, 703.6 kN, and 7.7%, respectively; and (v) the recommended XGBoost model could offer the alternative approach to determine Pmax of FRP-confined corroded RC columns for design practices, in addition to the current mechanics-based design models.

Nomogram for predicting early shoulder joint dysfunction after breast cancer surgery

AbstractObjectiveThis study aims to construct a clinical risk profile nomogram model for predicting early shoulder joint dysfunction (SJD) after breast cancer surgery.MethodsUsing a convenience sampling method, the clinical data of 161 breast cancer patients between February 2022 and July 2023 at Affiliated Cancer Hospital of Nanjing Medical University were selected and analyzed retrospectively. Risk factors were identified using univariate and multivariate logistic regression analyses. The R software was used to construct the risk prediction model and to plot the nomogram for early SJD post‐breast cancer surgery. The model's predictive performance was evaluated using the receiver operating characteristic (ROC) curve and the Hosmer–Lemeshow test.ResultsAmong the 161 patients, 104 (64.6%) experienced SJD. Multivariate logistic regression analysis finally included the functional exercise compliance scale for postoperative breast cancer patients (FECSPBCP), the compliance scale of physical exercise, the body mass index of patients, involved side to‐hand dominance, and the operation mode of breast and lymph nodes into the model. The area under the ROC curve (AUC) was 0.785 (95% confidence interval: 0.711–0.860), indicating a good model fit as confirmed by the Hosmer–Lemeshow test (X2 = 3.2891, p = .9149). Internal validation using the Bootstrap resampling method (n = 1000) yielded an AUC of 0.731.ConclusionsThe incidence of early SJD was high among postoperative breast cancer patients. The constructed risk prediction model can assist medical professionals in the early identification of high‐risk individuals and provide targeted interventions to prevent long‐term disabilities.