Training Set Research Articles

Predicting the financial performance of microfinance institutions with machine learning techniques

Purpose Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs). Design/methodology/approach This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python. Findings The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs. Research limitations/implications This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities. Originality/value This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

Development and Validation of a Clinical Predictive Nomogram for Assessing the Risk of Recurrence of Acute Pancreatitis in Combined Hypertriglyceridemia.

The objective of this study is to develop and validate a new nomogram-based scoring system for anticipating the recurrence of acute pancreatitis (AP) in combined hypertriglyceridemia (HTG). A total of 292 patients diagnosed with AP combined with HTG participated in this research. Among them, 201 patients meeting the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Clinical data were collected for all patients. In the training set, predictive indicators were chosen through backward stepwise multivariable logistic regression analysis. Subsequently, a nomogram was developed based on the selected indicators. Finally, the model's performance was validated in both the training and validation sets. By employing backward stepwise multivariable logistic regression analysis, we identified diabetes, gallstones, alcohol consumption, and triglyceride levels as predictive indicators. Subsequently, a clinical nomogram that incorporates these four independent risk factors was constructed. Model validation demonstrated an AUC of 0.726 (95% CI 0.644-0.809) in the training set and an AUC of 0.712 (95% CI 0.583-0.842) in the validation set, indicating a good discriminative ability. The Hosmer-Lemeshow test yielded P-values of 0.882 and 0.536 in the training and validation sets, respectively, suggesting good calibration. Calibration curves further confirmed good agreement. Ultimately, decision curve analysis (DCA) emphasized the clinical utility of our model. We have developed a nomogram for predicting the recurrence of AP combined with HTG in patients, and this nomogram demonstrates good discriminative ability, calibration, and clinical utility. This tool holds the potential to assist clinicians in offering more personalized treatment strategies for AP combined with HTG.

Predicting Sprint Performance From the Vertical and Horizontal Jumps in National Football League Combine Athletes.

Agar-Newman, DJ, MacRae, F, Tsai, M-C, and Klimstra, M. Predicting sprint performance from the vertical and horizontal jumps in National Football League Combine athletes. J Strength Cond Res 38(8): 1433-1439, 2024-Identifying fast athletes is an important part of the National Football League (NFL) Combine. However, not all athletes partake in the 36.58-m sprint, and relying on this single test may miss potentially fast athletes. Therefore, the purpose of this study was to determine whether sprinting times can be predicted using simple anthropometric and jumping measures. Data from the NFL Combine between the years 1999-2020 inclusive were used (n = 4,149). Subjects had a mean (±SD) height = 1.87 ± 0.07 m and body mass = 111.96 ± 20.78 kg. The cross-validation technique was used, partitioning the data into a training set (n = 2,071) to develop regression models to predict time over the 9.14-, 9.14- to 18.29-, 18.29- to 36.58-m, and 36.58-m segments using vertical jump, broad jump, height, and mass as the independent variables. The models were then evaluated against a test set (n = 2,070) for agreement. Statistically significant (p < 0.01) models were determined for 9.14-m time (adjusted R2 = 0.76, SEE = 0.05 seconds), 9.14- to 18.29-m time (adjusted R2 = 0.74, SEE = 0.04 seconds), 18.29- to 36.59-m time (adjusted R2 = 0.79, SEE = 0.07 seconds), and 36.58-m time (adjusted R2 = 0.84, SEE = 0.12 seconds). When evaluated against the test set, the models showed biases of -0.05, -0.04, -0.02, and -0.02 seconds and root-mean-square error of 0.07, 0.05, 0.07, and 0.12 seconds for the 9.14-, 9.14- to 18.29-, 18.29- to 36.58-m, and 36.58-m segments, respectively. However, 5-6% of the predictions lay outside of the limits of agreement. This study provides 4 formulae that can be used to predict sprint performance when the 36.58-m sprint test is not performed, and practitioners can use these equations to determine training areas of opportunity when working with athletes preparing for the NFL Combine.

Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm.

The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.

A deep learning approach based on domain generalization for mooring tension prediction in floating structures

Generalization remains a significant challenge in the application of deep learning methods, particularly in predicting mooring line tension for moored floating structures. The dynamic and complex nature of marine environments makes accurate prediction of mooring line tension a formidable task. Existing deep learning models, constrained by the data distribution of their training sets, typically perform well within the scope of these datasets. However, their limited generalization capability becomes apparent when confronted with unknown and changing marine conditions, resulting in a decline in prediction accuracy. To address this issue, this paper proposes a Domain Generalization (DG) model approach, which incorporates a distribution matching regularization term to the model's loss function, aimed at reducing distributional disparities between data from different domains. The proposed method was compared with existing Deep Neural Network (DNN) model method through validation with engineering case studies. The results indicate that the DG model method significantly surpasses the DNN model in terms of generalization capabilities. The mooring line tension prediction model developed herein holds potential for constructing digital twins, offering near real-time data support for moored floating engineering projects. This facilitates timely and accurate decision-making in response to environmental changes.

A Six-gene Prognostic Model Based on Neutrophil Extracellular Traps (NETs)-related Gene Signature for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant cancers. Neutrophil extracellular traps (NETs) have been discovered to play a crucial role in the pathogenesis of LUAD. We aimed to establish an innovative prognostic model for LUAD based on the distinct expression patterns of NETs-related genes. The TCGA LUAD dataset was utilized as the training set, while GSE31210, GSE37745, and GSE50081 were undertaken as the verification sets. The patients were grouped into clusters based on the expression signature of NETs-related genes. Differentially expressed genes between clusters were identified through the utilization of the random forest and LASSO algorithms. The NETs score model for LUAD prognosis was developed by multiplying the expression levels of specific genes with their corresponding LASSO coefficients and then summing them. The validity of the model was confirmed by analysis of the survival curves and ROC curves. Additionally, immune infiltration, GSEA, mutation analysis, and drug analysis were conducted. Silencing ABCC2 in A549 cells was achieved to investigate its effect. We identified six novel NETs-related genes, namely UPK1B, SFTA3, GGTLC1, SCGB3A1, ABCC2, and NTS, and developed a NETs score signature, which exhibited a significant correlation with the clinicopathological and immune traits of the LUAD patients. High-risk patients showed inhibition of immune-related processes. Mutation patterns exhibited variability among the different groups. AZD3759, lapatinib, and dasatinib have been identified as potential candidates for LUAD treatment. Moreover, the downregulation of ABCC2 resulted in the induction of apoptosis and suppression of migration and invasion in A549 cells. Altogether, this study has identified a novel NET-score signature based on six novel NET-related genes to predict the prognosis of LUAD and ABCC2 and has also explored a new method for personalized chemo-/immuno-therapy of LUAD.

The Role of Artificial Intelligence in Predicting Optic Neuritis Subtypes From Ocular Fundus Photographs.

Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss. It is important to distinguish MS ON from other ON subtypes early, to guide appropriate management. Yet, identifying ON and differentiating subtypes can be challenging as MRI and serological antibody test results are not always readily available in the acute setting. The purpose of this study is to develop a deep learning artificial intelligence (AI) algorithm to predict subtype based on fundus photographs, to aid the diagnostic evaluation of patients with suspected ON. This was a retrospective study of patients with ON seen at our institution between 2007 and 2022. Fundus photographs (1,599) were retrospectively collected from a total of 321 patients classified into 2 groups: MS ON (262 patients; 1,114 photographs) and non-MS ON (59 patients; 485 photographs). The dataset was divided into training and holdout test sets with an 80%/20% ratio, using stratified sampling to ensure equal representation of MS ON and non-MS ON patients in both sets. Model hyperparameters were tuned using 5-fold cross-validation on the training dataset. The overall performance and generalizability of the model was subsequently evaluated on the holdout test set. The receiver operating characteristic (ROC) curve for the developed model, evaluated on the holdout test dataset, yielded an area under the ROC curve of 0.83 (95% confidence interval [CI], 0.72-0.92). The model attained an accuracy of 76.2% (95% CI, 68.4-83.1), a sensitivity of 74.2% (95% CI, 55.9-87.4) and a specificity of 76.9% (95% CI, 67.6-85.0) in classifying images as non-MS-related ON. This study provides preliminary evidence supporting a role for AI in differentiating non-MS ON subtypes from MS ON. Future work will aim to increase the size of the dataset and explore the role of combining clinical and paraclinical measures to refine deep learning models over time.

Machine learning assisted designing of Y-series small molecule acceptors: Library generation and property prediction

The designing of new small molecule acceptors (SMAs) for organic solar cells has been a prominent area of research for many decades. It is challenging to find unique materials due to expensive experimentation. Machine learning emerges as a promising tool for rapid and cost-effective prediction of properties. In the current study, electron affinity is predicted using machine learning models. Molecular descriptors are calculated for model training. Four machine learning models are used. A reasonably high predictive capability is obtained for random forest model (r-squared values of 0.92 and 0.82 for training and test set, respectively). Furthermore, the chemical database of small molecule acceptors is generated. Generated virtual space is anticipated by exploiting the t-distributed stochastic neighbor embedding (t-SNE) method. Structure Activity Landscape Index (SALI) analysis is conducted to examine how properties change with structural variations. A minimal change in electron affinity resulted from structural modifications. Additionally, clustering analysis is performed on selected small molecule acceptors for structural grouping of SMAs. Five SMAs with lowest synthetic accessibility (SA) score are selected for density functional theory (DFT) calculations. The introduced multiple dimensional framework has potential screened the materials in short-time and efficient way.

A two-stage approach for aircraft detection with convolutional neural network

Over the past few years, object detection has experienced remarkable advancements, primarily attributable to significant progress in deep learning architectures. Nonetheless, the task of identifying aircraft targets within remote sensing images remains a challenging and actively explored area. Presently, there are two main approaches employed for this task: one utilizing convolutional neural network (CNN) techniques and the other relying on conventional methods. In this work, a CNN based architecture is proposed to recognize aircraft types using remote sensing images. The experiments performed on multi-type aircraft remote sensing images (MTARSI) dataset show that the proposed architecture achieves 97.07%, 94.81%, and 94.44% accuracy rates for training, validation and testing sets. The results approve that, the architecture outperforms state of the art models.

Determinantal point process attention over grid cell code supports out of distribution generalization.

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.

Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence

ObjectiveTo predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features and clinical data from pelvic magnetic resonance imaging (MRI).Methods52 vulvar...

Evaluation of CD3 and CD8 T-Cell Immunohistochemistry for Prognostication and Prediction of Benefit From Adjuvant Chemotherapy in Early-Stage Colorectal Cancer Within the QUASAR Trial.

High densities of tumor infiltrating CD3 and CD8 T-cells are associated with superior prognosis in colorectal cancer (CRC). Their value as predictors of benefit from adjuvant chemotherapy is uncertain. Tumor tissue from 868 patients in the QUASAR trial (adjuvant fluorouracil/folinic acid v observation in stage II/III CRC) was analyzed by CD3 and CD8 immunohistochemistry. Pathologists, assisted by artificial intelligence, calculated CD3 and CD8 cell densities (cells/mm2) in the core tumor (CT) and invasive margin (IM). Participants were randomly partitioned into training and validation sets. The primary outcome was recurrence-free interval (RFI), 2-year RFI for assessment of biomarker-treatment interactions. Maximum-likelihood methods identified optimal high-risk/low-risk group cutpoints in the training set. Prognostic analyses were repeated in the validation set. In the training set, the recurrence rate in the high-risk group was twice that in the low-risk group for all measures (CD3-CT: rate ratio [RR], 2.00, P = .0008; CD3-IM: 2.38, P < .00001; CD8-CT: 2.17, P = .0001; CD8-IM: 2.13, P = .0001). This was closely replicated in the validation set (RR, 1.96, 1.79, 1.72, 1.72, respectively). In multivariate analyses, prognostic effects were similar in colon and rectal cancers, and in stage II and III disease. Proportional reductions in recurrence with adjuvant chemotherapy were of similar magnitude in the high- and low-recurrence risk groups. Combining information from CD3-IM and CD3-CT (CD3 Score) generated high-, intermediate-, and low-risk groups with numbers needed to treat (NNTs) to prevent one disease recurrence being 11, 21, and 36, respectively. Recurrence rates in the high-risk CD3/CD8 groups are twice those in the low-risk groups. Proportional reductions with chemotherapy are similar, allowing NNTs derived in QUASAR to be updated using contemporary, nonrandomized data sets.

AI-generated text detection and classification based on BERT deep learning algorithm

With the rapid development and wide application of deep learning technology, AI-generated text detection plays an increasingly important role in various fields. In this study, we developed an efficient AI-generated text detection model based on the BERT algorithm, which provides new ideas and methods for solving related problems. In the data preprocessing stage, a series of steps were taken to process the text, including operations such as converting to lowercase, word splitting, removing stop words, stemming extraction, removing digits, and eliminating redundant spaces, to ensure data quality and accuracy. By dividing the dataset into a training set and a test set in the ratio of 60% and 40%, and observing the changes in the accuracy and loss values during the training process, we found that the model performed well during the training process. The accuracy increases steadily from the initial 94.78% to 99.72%, while the loss value decreases from 0.261 to 0.021 and converges gradually, which indicates that the BERT model is able to detect AI-generated text with high accuracy and the prediction results are gradually approaching the real classification results. Further analysis of the results of the training and test sets reveals that in terms of loss value, the average loss of the training set is 0.0565, while the average loss of the test set is 0.0917, showing a slightly higher loss value. As for the accuracy, the average accuracy of the training set reaches 98.1%, while the average accuracy of the test set is 97.71%, which is not much different from each other, indicating that the model has good generalisation ability. In conclusion, the AI-generated text detection model based on the BERT algorithm proposed in this study shows high accuracy and stability in experiments, providing an effective solution for related fields. In the future, the model performance can be further optimised and its potential for application in a wider range of fields can be explored to promote the development and application of AI technology in the field of text detection.

Development and validation of a predictive model for pulmonary infection risk in patients with traumatic brain injury in the ICU: a retrospective cohort study based on MIMIC-IV

ObjectiveTo develop a nomogram for predicting occurrence of secondary pulmonary infection in patients with critically traumatic brain injury (TBI) during their stay in the intensive care unit, to further optimise...

Development and validation of prognostic nomogram in pediatric melanoma: a population-based study.

The aim of the study is to use the Surveillance, Epidemiology, and End Results (SEER) database to develop a useful clinical nomogram that uses prognosis prediction for pediatric melanoma patients. We obtained clinical information on pediatric melanoma patients from the SEER database between 2000 and 2018. Each patient was split into a training cohort or a validation cohort at random. Results between various subgroups were compared using Kaplan-Meier analyses. We created a nomogram to calculate the probability of survival for pediatric patients with melanoma. The performance of nomograms was assessed using calibration and discrimination. To assess the clinical use of this newly created model, decision curve analysis was also performed. In this study, a total of 890 eligible patients were chosen at random and allocated to 70% of training cohorts (n = 623) and 30% of validation cohorts (n = 267). After applying the chosen various components to create a nomogram, validated indexes showed that the nomogram had a strong capacity for discrimination. The training set's and validation set's C-index values were 0.817 and 0.832, respectively. The calibration plots demonstrated a strong correlation between the observation and the forecast. The model has a good clinical net benefit for pediatric melanoma patients, according to the clinical decision curve. In conclusion, we created an effective survival prediction model for pediatric melanoma. This nomogram is accurate and useful for clinical decision-making. Still, more external confirmation is required.

Study on hysteresis performance of four-limb CFST latticed column-box girder joints based on GA-BP neural network

This study investigates the seismic performance and energy dissipation capacity of the concrete-filled steel tube (CFST) latticed column-box girder joints. A low-cycle reciprocating load test was carried out on the CFST latticed column-box girder joints with three different connection forms. Based on the test results, a genetic algorithm (GA) is developed in combination with the back propagation neural network (BPNN) model for prediction research. Using a training set of joints with single-limb diagonal and cross-diagonal braces (training set), the development of load-displacement hysteresis curves of joints with transverse diaphragm plate (test set) under low-cycle reciprocating loads is predicted. The test results are compared with those obtained from the traditional algorithms and finite element simulations. The results on the joint hysteresis performance show that all three types of joints have a good seismic performance and energy dissipation capacity. Compared with the traditional algorithms, the GA-BP model predicts more accurate results and is therefore used to predict the load-displacement hysteresis curves of CFST latticed column-box girder joints, which is shown to be convenient and feasible.

The potential value of dual-energy CT radiomics in evaluating CD8+, CD163+ and αSMA+ cells in the tumor microenvironment of clear cell renal cell carcinoma.

This study aims to develop radiomics models and a nomogram based on machine learning techniques, preoperative dual-energy computed tomography (DECT) images, clinical and pathological characteristics, to explore the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC). We retrospectively recruited of 87 patients diagnosed with ccRCC through pathological confirmation from Center I (training set, n = 69; validation set, n = 18), and collected their DECT images and clinical information. Feature selection was conducted using variance threshold, SelectKBest, and the least absolute shrinkage and selection operator (LASSO). Radiomics models were then established using 14 classifiers to predict TME cells. Subsequently, we selected the most predictive radiomics features to calculate the radiomics score (Radscore). A combined model was constructed through multivariate logistic regression analysis combining the Radscore and relevant clinical characteristics, and presented in the form of a nomogram. Additionally, 17 patients were recruited from Center II as an external validation cohort for the nomogram. The performance of the models was assessed using methods such as the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). The validation set AUC values for the radiomics models assessing CD8+, CD163+, and αSMA+ cells were 0.875, 0.889, and 0.864, respectively. Additionally, the external validation cohort AUC value for the nomogram reaches 0.849 and shows good calibration. Radiomics models could allow for non-invasive assessment of TME cells from DECT images in ccRCC patients, promising to enhance our understanding and management of the tumor.

Deep Learning Based Automatic Left Ventricle Segmentation from the Transgastric Short-Axis View on Transesophageal Echocardiography: A Feasibility Study

Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management.

Factors influencing accelerated aging in patients with type 2 diabetes mellitus and coronary heart disease

ObjectiveTo investigate the factors influencing accelerated aging in patients with type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD).MethodsA total of 216 patients diagnosed with T2DM and CHD between August 2019 and August 2023 at Xuzhou Central Hospital were selected. Patients were divided into an aging group and a non-aging group, based on the positive or negative values of phenotypic age acceleration (PhenoAgeAccel). Logistic regression analysis was conducted. Variables that had a univariate analysis P&amp;lt; 0.05 were included in the multivariate analysis to identify factors influencing aging in patients with T2DM and CHD, and the area under the curve of the model was reported.ResultsThis study included 216 patients, with 89 in the accelerated aging group, and 127 in the non-accelerated aging group. The average age of patients was 70.40 (95% CI: 69.10-71.69) years, with 137 males (63.4%). Compared with the non-accelerated aging group, patients in the accelerated aging group were older, with a higher proportion of males, and a higher prevalence of hypertension, stable angina pectoris, and unstable angina pectoris. Multivariate Logistic regression analysis indicated that the absolute value of neutrophils (NEUT#), urea (UREA), adenosine deaminase (ADA), and the triglyceride-glucose index (TyG) were risk factors for accelerated aging, while cholinesterase (CHE) was a protective factor. For each unit increase in NEUT#, UREA, ADA, and TyG, the risk of aging increased by 64%, 48%, 10%, and 789%, respectively. The overall area under the receiver operating characteristic (ROC) curve of the model in the training set was 0.894, with a 95% confidence interval (CI) of 0.851-0.938.ConclusionNEUT#, CHE, UREA, ADA, and TyG are predictors of accelerated aging in patients with T2DM and CHD, with the model showing favorable overall predictive performance.

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC) and predicting bone metastasis using magnetic resonance imaging (MRI). MRI radiomic features were extracted and analyzed to identify malignant lesions. A total of 86 patients (44 with benign lesions without bone invasion and 42 with malignant lesions with bone invasion) were included. Data and clinical information were managed using the RadCloud Platform (Huiying Medical Technology Co., Ltd., Beijing, China). The study employed a hand-crafted radiomics model, with the dataset randomly split into training and validation sets in an 8:2 ratio using 815 random seeds. The results revealed that the ML method support vector machine (SVM) performed best for detecting bone invasion (AUC = 0.999) in the test set. Radiomics tumor features derived from MRI are useful to predicting bone invasion from oral squamous cell carcinoma with high accuracy. This study introduces an ML model utilizing SVM and radiomics to predict bone invasion in OSCC. Despite the promising results, the small sample size necessitates larger multicenter studies to validate and expand these findings.