Multiple Algorithms Research Articles

Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.

Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomic aging clocks from nuclear magnetic resonance spectroscopy data. The UK Biobank data, including 168 plasma metabolites from up to N = 225,212 middle-aged and older adults (mean age, 56.97 years), were used to train and internally validate 17 algorithms. Metabolomic age (MileAge) delta, the difference between metabolite-predicted and chronological age, from a Cubist rule-based regression model showed the strongest associations with health and aging markers. Individuals with an older MileAge were frailer, had shorter telomeres, were more likely to suffer from chronic illness, rated their health worse, and had a higher all-cause mortality hazard (HR = 1.51; 95% CI, 1.43 to 1.59; P < 0.001). This metabolomic aging clock (MileAge) can be applied in research and may find use in health assessments, risk stratification, and proactive health tracking.

EvaluationMaster: A GUI Tool for Structure-Based Virtual Screening Evaluation Analysis and Decision-Making Support.

Structure-based virtual screening (SBVS) plays an indispensable role in the early phases of drug discovery, utilizing computational docking techniques to predict interactions between molecules and biological targets. During the SBVS process, selecting appropriate target structures and screening algorithms is crucial, as these choices significantly shape the outcomes. Typically, such selections require researchers to be proficient with multiple algorithms and familiar with evaluation and analysis processes, complicating their tasks. These algorithms' lack of graphical user interfaces (GUIs) further complicates it. To address these challenges, we introduced EvaluationMaster, the first GUI tool designed specifically to streamline and standardize the evaluation and decision-making processes in SBVS. It supports four docking algorithms' evaluation under multiple target structures and offers a comprehensive platform that manages the entire workflow─including the downloading of molecules, construction of decoy datasets, prediction of protein pockets, batch docking, and extensive data analysis. By automating complex evaluation tasks and providing clear visualizations of analysis results, EvaluationMaster significantly reduces the learning curve for researchers and boosts the efficiency of evaluations, potentially improving SBVS hit rates and accelerating the discovery and development of new therapeutic agents.

Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach

Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems. Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition. However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets featuring light makeup faces. If these instances are incorrectly flagged as fraudulent by face anti-spoofing systems, it could lead to user inconvenience. In response, we develop a face anti-spoofing database that includes light makeup faces and establishes a criterion for determining light makeup to select appropriate data. Building on this foundation, we assess multiple established face anti-spoofing algorithms using the newly created database. Our findings reveal that the majority of these algorithms experience a decrease in performance when faced with light makeup faces. Consequently, this paper introduces a general face anti-spoofing algorithm specifically designed for light makeup faces, which includes a makeup augmentation module, a batch channel normalization module, a backbone network updated via the Exponential Moving Average (EMA) method, an asymmetric virtual triplet loss module, and a nearest neighbor supervised contrastive module. The experimental outcomes confirm that the proposed algorithm exhibits superior detection capabilities when handling light makeup faces.

Evaluate the renal system damage caused by zoledronic acid: a comprehensive analysis of adverse events from FAERS

BackgroundZoledronic acid (ZA) is widely used for the treatment of osteolytic bone metastases in malignancies and osteoporosis, but it has been associated with renal impairment. In this study, we investigated adverse events (AEs) related to renal and urinary system diseases associated with ZA using the U. S. FDA’s Adverse Event Reporting System.MethodsWe collected FAERS data from Q1 2004 to Q1 2024 and used the reporting odds ratio to detect AEs related to renal and urinary system diseases associated with ZA. Additionally, we applied multiple algorithms, including ROR, proportional reporting ratio, bayesian confidence propagation neural network, and multi-item gamma poisson shrinker, to quantify renal and urinary AEs under different indications.ResultsA total of 52,495 AE reports involving ZA as the primary suspect drug were identified. Among renal and urinary system diseases, 25 distinct AEs were recognized, with renal tubular necrosis being the most frequently reported. For different indications, renal tubular necrosis was the most reported AE in breast cancer and osteoporosis; nephrogenic diabetes insipidus was both the most frequent and strongest signal in lung cancer; proteinuria was most common in multiple myeloma, and polyuria in prostate cancer. Furthermore, most AEs occurred in patients who had been on ZA for more than 360 days, followed by those within the first 30 days of use.ConclusionBased on pharmacovigilance data from FAERS, different renal and urinary system AEs should be closely monitored and addressed according to the specific indications for which ZA is used.

VOLUME SINGULAR INTEGRAL EQUATIONS FOR PROBLEMS OF LOW-FREQUENCY SCATTERING OF ELECTROMAGNETIC WAVES IN ANISOTROPIC STRUCTURES

This paper deals with volume singular integral equations describing the problems of low-frequency scattering of electromagnetic waves in bounded three-dimensional anisotropic structures. The spectrum of integral operators is studied. The domain of the operator spectrum on the complex plane for the low-frequency case is presented explicitly. The generalized method of simple iteration is described, for application of which it is necessary to know the area of the operator spectrum on the complex plane. The collocation method on a uniform grid is used to discretize the integral equations. This allows, using a fast discrete Fourier transform, to construct an algorithm for fast multiplication of the matrix of a system of linear equations by a vector. The results of numerical solution of the considered problems are given.

Integrative Analysis of Pharmacology and Transcriptomics Predicts Resveratrol Will Ameliorate Microplastics-Induced Lung Damage by Targeting Ccl2 and Esr1

Background: Microplastics (MPs) are ubiquitous on earth, posing a growing threat to human health. Previous studies have shown that the lung is a primary organ for MPs exposure. Resveratrol (RES) is a common dietary polyphenol that exhibits anti-inflammatory and antioxidant effects. However, whether RES exerts a protective effect against MPs-induced lung damage is still unknown. Methods: The targets of RES were retrieved from five databases. Differentially expressed genes (DEGs) were identified through comprehensive bioinformatic analysis. Multiple algorithms were employed to screen for the core targets. Ultimately, molecular docking analysis and molecular dynamics (MD) simulations were utilized to confirm the binding affinity between RES and the core targets. Results: In total, 1235 DEGs were identified in the transcriptomes. After removing duplicates, a total of 739 RES targets were obtained from five databases, and 66 of these targets intersected with DEGs. The potential core targets (Esr1, Ccl2) were further identified through topological analysis and machine learning. These findings were subsequently verified by molecular docking and MD simulations. Conclusions: This study demonstrated that RES may mitigate lung injury induced by MPs by targeting Esr1 and Ccl2. Our research offers a novel perspective on the prevention and treatment of MPs-induced lung injury.

A Study on Wild and Domestic Animal Detection for Farm Protection by using Computer Vision

Crop protection against wild animal intrusion has become a pressing challenge with significant social and economic implications, particularly in agricultural-dependent nations like India. In response, an innovative AI-driven surveillance system is designed to detect and determine potential threats from animals to farm environments. The system accurately identifies and classifies animals in farm images by leveraging advanced computer vision techniques and machine learning algorithms, including Support Vector Machines, K-Means clustering, Random Forest, Decision Trees, and Logistic Regression. The model generalizes effectively by analyzing a diverse dataset comprising various animal species. Key features such as accuracy, precision, recall, F1-score, and confusion matrices are employed to assess model performance comprehensively. The results showcase high accuracy across multiple algorithms. The proposed system offers a promising solution to protect crops, minimize losses, and foster harmonious coexistence between farming and wildlife. The results demonstrate good accuracy for various algorithms: 92.75% for Logistic Regression, 86.47% for Decision Trees, 95.65% for Random Forests, and 94.20% for Support Vector Machines. This highlights how reliable the system is in classifying animals, providing a viable way to safeguard crops, reduce losses, and promote peaceful cohabitation between agriculture and wildlife.

A Honey Bee In-and-Out Counting Method Based on Multiple Object Tracking Algorithm

The honey bee (Apis mellifera) is of great significance to both the ecological environment and human society, providing bee products and making a significant contribution to the pollination of crops [...]

Downlink Non-Orthogonal Multiple Access Power Allocation Algorithm Based on Double Deep Q Network for Ensuring User’s Quality of Service

Downlink Non-Orthogonal Multiple Access Power Allocation Algorithm Based on Double Deep Q Network for Ensuring User’s Quality of Service

Image Based Detection of Coating Wear on Cutting Tools With Machine Learning

Wear of cutting tools is known to affect the surface integrity of the workpiece and significantly contributes to machine downtime. To establish wear-resistant cutting tools, several coating strategies have been introduced. It has been shown that the wear rate increases dramatically once the coating is worn through. Detecting coating layer loss is therefore a good indicator of the remaining useful life of the cutting tool. Based on cutting experiments conducted with a TiN/AlTiN-coated cutting tool, an image dataset was generated and pre-processed using multiple algorithms, such as Canny edge detection and Hough line transforms. For the classification task four machine learning Algorithms consisting of Random Forests, Decision Trees, Support Vector Machines and a Feed Forward Neural Network were implemented. The results demonstrate that all four algorithms lead to a good classification performance, with Decision Trees showing the best performance with a F1-score of 0.95. Therefore, this research provides an efficient data processing and classification framework for detecting coating wear on cutting tools.

One-Stage Anchor-Free Online Multiple Target Tracking With Deformable Local Attention and Task-Aware Prediction.

The tracking-by-detection paradigm currently dominates multiple target tracking algorithms. It usually includes three tasks: target detection, appearance feature embedding, and data association. Carrying out these three tasks successively usually leads to lower tracking efficiency. In this paper, we propose a one-stage anchor-free multiple task learning framework which carries out target detection and appearance feature embedding in parallel to substantially increase the tracking speed. This framework simultaneously predicts a target detection and produces a feature embedding for each location, by sharing a pyramid of feature maps. We propose a deformable local attention module which utilizes the correlations between features at different locations within a target to obtain more discriminative features. We further propose a task-aware prediction module which utilizes deformable convolutions to select the most suitable locations for the different tasks. At the selected locations, classification of samples into foreground or background, appearance feature embedding, and target box regression are carried out. Two effective training strategies, regression range overlapping and sample reweighting, are proposed to reduce missed detections in dense scenes. Ambiguous samples whose identities are difficult to determine are effectively dealt with to obtain more accurate feature embedding of target appearance. An appearance-enhanced non-maximum suppression is proposed to reduce over-suppression of true targets in crowded scenes. Based on the one-stage anchor-free network with the deformable local attention module and the task-aware prediction module, we implement a new online multiple target tracker. Experimental results show that our tracker achieves a very fast speed while maintaining a high tracking accuracy.

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms. Glaucoma has afflicted many patients for many years, with excessive pressure in the eye continuously damaging the nervous system and leading to severe blindness. An effective molecular diagnostic method is currently lacking. The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes. A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database. The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Least absolute shrinkage and selection operator regression model (LASSO), and Support Vector Machines model (SVM). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on these genes, we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples by applying CIBERSORT method. 8 hub genes were determined: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158 and GSN. The diagnostic model showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma. Our results systematically determined 8 hub genes and established a molecular diagnostic model that allowed the diagnosis of Glaucoma. Our study provided a basis for future systematic studies of Glaucoma pathogenesis.

Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease. The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Study on progressive collapse of overall structure based on numerical simulation method and prediction of structural collapse

This study investigated the structural progressive collapse using finite element (FE) and theoretical methods. Firstly, an FE model was established based on the component method and layered shell element method. The reliability of modeling approaches was evaluated by comparing them with the previous experimental tests by the author. The NIST building was taken as an example to investigate the structural collapse behavior and the failure modes after different component failure scenarios including single column, double columns, multiple columns, and horizontal components based on the alternative path method. The results indicated that the failure of gravity columns and joints more easily triggered the progressive collapse of the structure. After the failure of double moment-resisting frame columns, the structure still demonstrated considerable robustness. The impact of floor fragments played a controlling role in the process of collapse propagation. A method for achieving rapid collapse of structures was proposed based on the structural collapse characteristics. The study found that a demolition strategy based on gravity columns can facilitate the rapid collapse of structures. After comparing the different simplified modeling methods, it was revealed that the 2D models underestimated the anti-collapse performance of structures compared to the 3D models. Under the same scenarios, the predicted structural collapse response of the 2D model was several times greater than that of the 3D model (up to 7 times). Meanwhile, the structural collapse occurred earlier in the 2D model. The anti-collapse performance of structures adopting different design codes also was compared. It was revealed that GSA 2016 has put forward requirements for higher collapse resistance of buildings. In this study, a new calculation method was proposed, namely the Collapse Probability Prediction Method Based on the Randomness of Structural Load (CPPM-RSL). In the CPPM-RSL, load reliability theory was employed to ensure that the structural collapse resistance design fell within the realm of structural engineering theory. Meanwhile, the CPPM-RSL offered greater engineering interpretability compared to previous methods. Additionally, a new simplified mechanical model was proposed to predict the collapse resistance of structures. The new simplified model did not depend on the computational results obtained from the finite element method (FEM) unlike the existing model. At the same time, the simplified method also exhibited high reliability as revealed by comparisons with the FEM. To solve highly nonlinear problems, machine learning (ML) algorithms were established to predict the anti-collapse performance and the collapse probability of structures based on the proposed theoretical model. Meanwhile, the applicability and performance of the multiple ML algorithms in estimating the structural collapse behavior were compared and evaluated. The results indicated that the LightGBM model exhibited stronger robustness and predictive ability.

Molecular networks via reduced reverse degree approach

Porphyrazine and tetrakis porphyrazine are examples of organic compounds with complicated structures of rings. Physicists and chemical researchers have been interested in these structures because of their highly conjugated systems, which lead to peculiar optical and electrical characteristics. These structures are the fundamental components of molecular electronics, sensors, functional materials, and catalysis, among other scientific fields. The idea behind modeling molecules as networks is to calculate the topological index, where atoms are nodes and bonds are links. We can use multiple techniques and algorithms to calculate the topological index. We have used the reduced reverse degree-based approach for estimating the topological indices of the Porphyrazine and Tetrakis porphyrazine structures. The purpose of calculating the reduced reverse degree-based topological indices is to quantify the molecular topology of the mentioned structures. In future research, we can also use these indices in SAR/QSAR modeling of porphyrazine and tetrakis porphyrazine. These indices can also provide comparative analysis and descriptors for predicting chemical behavior, which is useful in material science applications and drug designs. In this study, we present a formula for calculating reduced reverse degree-based topological indices for porphyrazine and tetrakis porphyrazine, including the reduced reverse geometric arithmetic index, reduced reverse general Randić index, reduced reverse Balaban index, reduced reverse redefined Zagreb index, reduced reverse forgotten index, reduced reverse hyper-Zagreb index, and reduced reverse atom-bond connectivity index. Before the conclusion, there is a graph-theoretical analysis and comparison to ascertain the essential significance and validate the obtained results. This research helps to create novel materials for a variety of applications and sheds light on the structural and chemical characteristics of these molecular networks.

Numerical analysis and data-driven optimization of energy harvesting efficiency from wake-induced vibration in a multi-cylinder configuration

Wake-induced vibrations (WIV) in multi-cylinder configurations have demonstrated greater energy harvesting efficiency in hydrokinetic applications compared to conventional vortex-induced vibrations (VIV) of a single cylinder. However, the complex fluid-structure interactions make it challenging to identify optimal configurations for maximum power output, as extensive simulations across numerous parameter combinations lead to substantial computational costs with traditional computational fluid dynamics (CFD) methods. To address this challenge, we developed a data-driven model using automated machine learning (AutoML) techniques, focusing on four key parameters: spacing, diameter, damping, and reduced velocity. Trained on comprehensive datasets from validated CFD simulations, this model integrates multiple algorithms to predict the power efficiency of WIV systems with high accuracy. Our approach enables rapid and precise evaluations of power efficiency across a broad range of configurations, significantly reducing the computational burden compared to traditional CFD approaches. The results indicate that optimal configurations, characterized by larger upstream cylinder diameters, higher damping ratios, and ideal spacing ratios, can achieve power generation efficiencies of up to 59.15%. Further analysis of vorticity contours reveals that synchronized interactions between upstream vortex shedding and downstream structure motions enhance WIV, thereby improving energy harvesting efficiency.

Copper metabolism-related lncRNAs predict prognosis and immune landscape in liver cancer patients.

Characterized by its high mortality and easy recurrence, hepatocellular carcinoma (HCC) poses significant clinical challenges. The association between copper metabolism and development of cancer has been identified. However, the underlying mechanisms of copper metabolism-related long non-coding RNAs (CMRLs) in HCC remain elusive. To address the gap, our study analyzed the prognostic and immuno-therapeutic value of CMRLs in HCC. This research utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) data (n=424) for analysis, applying the "limma" package in R software for differential gene analysis and construction of a prognostic signature. We validated the signature using training and validation groups stochastically divided at a ratio of 1:1 and assessed prognostic value via Kaplan-Meier, C-index, and receiver operating characteristic (ROC) curves. By multivariate Cox regression, independent prognostic indicators were identified, and a nomogram was formulated for survival forecasting. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses elucidated biological pathways, and the immune landscape was examined through multiple algorithms. Finally, drug sensitivity was determined from Genomics of Drug Sensitivity in Cancer (GDSC), with mutation analysis conducted via maftools. In this study, a predictive model based on four pivotal CMRLs (PRRT3-AS1, AC108752.1, AC092115.3, AL031985.3) significantly associated with HCC progression and prognosis was constructed and validated with the overall survival (OS) prediction area under the curve (AUC) values for 1, 3, and 5 years of 0.718, 0.688, and 0.669, respectively. The calibration curves and C-index values showed a solid prognostic ability of the nomogram. The high-risk group was notably higher than the low-risk group both in OS and tumor mutational burdens (TMBs). Moreover, functional annotation enrichment analysis of CMRLs revealed that the signature was mainly associated with mitotic function, chromosome, kinetochore, cell cycle, and oocyte meiosis. Furthermore, therapeutic drugs, including fluorouracil, afatinib, alpelisib, cedranib, crizotinib, erlotinib, gefitinib, and ipatasertib, were found to induce higher sensitivity in high-risk group. The prognostic signature consisting of four CMRLs displays an outstanding predictive performance and improves the precision of immuno-oncology.

Enhancing Cardiovascular Disease Prediction Accuracy through an Ensemble Machine Learning Approach

This study explores the efficacy of an ensemble machine learning approach, specifically a Voting Classifier combining Decision Tree, k-Nearest Neighbors, and Gaussian Naive Bayes classifiers, in predicting cardiovascular diseases (CVDs). Utilizing a dataset consisting of 70,000 clinical records, the model was rigorously tested through 5-fold cross-validation, achieving remarkable results with average accuracies, precision, recall, and F1-scores all exceeding 99%. The findings validate the hypothesis that ensemble models, due to their capacity to leverage multiple learning algorithms, provide superior prediction accuracy and reliability compared to single predictor models. This research not only confirms the effectiveness of ensemble methods in medical diagnostics but also highlights their potential to enhance decision-making in clinical settings. Given the model's success in identifying various stages of cardiovascular conditions with high accuracy, it offers significant implications for early intervention and personalized patient management. Future research should aim to validate these results across more diverse populations and explore the integration of additional predictive factors that could refine the model's applicability. This study contributes to the computational health field by demonstrating how advanced machine learning techniques can be effectively applied in predicting health outcomes.

Dynamic Matrix Multiplication Using Reinforcement Learning

Efficient matrix multiplication is a core computational task in machine learning, deep learning, and scientific computing, as it significantly impacts the performance of model training and inference. This project explores the use of reinforcement learning (RL) to dynamically select the optimal matrix multiplication algorithm based on input matrix characteristics. We compare traditional matrix multiplication methods, including Strassen’s algorithm, Winograd’s algorithm, and tiled matrix multiplication, and implement a Q- learning-based agent to choose the most efficient algorithm adaptively. Our RL-driven approach enables real-time algorithm selection, balancing computational speed and resource usage across different matrix types and sizes. Experimental results demonstrate the system's ability to reduce execution time by selecting algorithms suited to specific scenarios, providing insights into algorithm efficiency and adaptability for various data sizes. Furthermore, this research suggests practical optimization strategies for handling large-scale computations in machine learning applications, including using parallel processing, GPU acceleration, and parameter tuning to minimize computational bottlenecks. This project underscores the potential of integrating RL for adaptive computation within AI workflows, offering a scalable solution for enhancing matrix operation efficiency in data-intensive applications

Enhancing clinical decision-making in closed pelvic fractures with machine learning models.

Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML) algorithms to predict HI and death in patients with closed pelvic fractures and identify relevant risk factors. The retrospective study included 208 patients diagnosed with pelvic fractures and admitted to Suning Traditional Chinese Medicine Hospital between 2019 and 2023. Among these, 133 cases were identified as closed PFs. Patients with closed fractures were divided into a training set (n = 115) and a test set (n = 18). The training set was further stratified into two groups based on hemodynamic stability: Group A (patients with HI) and Group B (patients with hemodynamic stability). A total of 40 clinical variables were collected, and multiple machine learning algorithms were employed to develop predictive models, including logistic regression (LR), C5.0 Decision Tree (DT), Naive Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), random Forest (RF), and artificial neural network (ANN). Additionally, factor analysis was performed to assess the interrelationships between variables. The RF and LR algorithms outperformed traditional methods-such as central venous pressure (CVP) and intra-abdominal pressure (IAP) measurements-in predicting HI. The RF model achieved an average under the ROC (AUC) of 0.92, with an accuracy of 0.86, precision of 0.81, and an F1 score of 0.87. The LR model had an average AUC of 0.82 but shared the same accuracy, precision, and F1 score as the RF model. Key risk factors identified included TILE grade, heart rate (HR), creatinine (CR), white blood cell count (WBC), fibrinogen (FIB), and lactic acid (LAC), with LAC levels >3.7 and an injury severity score (ISS) >13 as significant predictors of HI and mortality. In conclusion, the RF and LR algorithms are effective in predicting HI and mortality risk in patients with closed PFs, enhancing clinical decision-making and improving patient outcomes.