Integrated Method Research Articles

The influence of linguistic profiling on perceived employability: a new application of the systems theory framework of career development

PurposeThe purpose of this paper is to clarify the influence of linguistic profiling on perceived employability. In doing so, we recognize multiple factors that can contribute to this relationship. Using the systems theory framework (STF) of career development, we categorize these factors based on whether they primarily reside within the societal system, the organizational system, or the individual system. Subsequently, we construct and present an open systems model that depicts the influence of linguistic profiling on perceived employability in context with other societal, organizational and individual factors.Design/methodology/approachThe STF provides a theoretical perspective on how to contextualize linguistic profiling and perceived employability within the scope of career development. We employed an integrative literature review method to locate existing research studies that investigated the influences of linguistic profiling and perceived employability. The literature search process, coupled with inclusion and exclusion criteria, resulted in 46 publications retained for analysis.FindingsFindings provided evidence of language ideologies that contribute to linguistic profiling globally. Linguistic profiling usually results in reduced perceived employability. There was variation in the research findings based on the context of the research studies. The context consisted of the societal, organizational and individual systems in which the research study was conducted. Findings supported our construction of an open systems model of the influence of linguistic profiling on perceived employability, which is rooted in the STF.Originality/valueThis study highlights the negative influence of linguistic profiling on perceived employability. It also demonstrates how the STF can be used to contextualize the linguistic profiling problem within societal and organizational systems.

Development, validation and application of single molecule molecular inversion probe based novel integrated genetic screening method for 29 common lysosomal storage disorders in India

BackgroundCurrent clinical diagnosis pathway for lysosomal storage disorders (LSDs) involves sequential biochemical enzymatic tests followed by DNA sequencing, which is iterative, has low diagnostic yield and is costly due to overlapping clinical presentations. Here, we describe a novel low-cost and high-throughput sequencing assay using single-molecule molecular inversion probes (smMIPs) to screen for causative single nucleotide variants (SNVs) and copy number variants (CNVs) in genes associated with 29 common LSDs in India.Results903 smMIPs were designed to target exon and exon–intron boundaries of targeted genes (n = 23; 53.7 kb of the human genome) and were equimolarly pooled to create a sequencing library. After extensive validation in a cohort of 50 patients, we screened 300 patients with either biochemical diagnosis (n = 187) or clinical suspicion (n = 113) of LSDs. A diagnostic yield of 83.4% was observed in patients with prior biochemical diagnosis of LSD. Furthermore, diagnostic yield of 73.9% (n = 54/73) was observed in patients with high clinical suspicion of LSD in contrast with 2.4% (n = 1/40) in patients with low clinical suspicion of LSD. In addition to detecting SNVs, the assay could detect single and multi-exon copy number variants with high confidence. Critically, Niemann-Pick disease type C and neuronal ceroid lipofuscinosis-6 diseases for which biochemical testing is unavailable, could be diagnosed using our assay. Lastly, we observed a non-inferior performance of the assay in DNA extracted from dried blood spots in comparison with whole blood.ConclusionWe developed a flexible and scalable assay to reliably detect genetic causes of 29 common LSDs in India. The assay consolidates the detection of multiple variant types in multiple sample types while having improved diagnostic yield at same or lower cost compared to current clinical paradigm.

Is Collaborative Care the Future of Medicine? Lessons Learned from the Care of Children with Colorectal Conditions

The treatment of patients with colorectal disorders requires care from a wide variety of medical and surgical specialties over the course of their lifetime. This is ideally handled by a collaborative center which facilitates the assessment and development of patient care among multiple specialties which can enhance the quality and implementation of treatment plans, improve communication among different specialties, decrease morbidity, and improve patient satisfaction and outcomes. This collaborative approach can serve as a model for other parts of medicine requiring a similar multi-disciplinary and integrated method of care delivery. We describe the process, as well as the lessons learned in developing such a program.

Exploring the Impact of Pandemic Measures on Airport Performance

The impact of COVID-19 measures on airport performance is obvious, and there have been numerous studies on this topic. However, most of these studies discuss prevention measures, the effects on airport operations, forecasts of economic impacts, changes in service quality, etc. There is a lack of research on the effects of various prevention measures on airport operations and the interrelationships between these measures. This study focuses on addressing this gap. In this study, an integrated approach is devised that combines the decision-making trial and evaluation laboratory (DEMATEL) method and interpretive structural modeling (ISM). This integrated method is useful for exploring the relationship between pandemic measures and airport performance as well as the complex relationship between them, and the combination of methods improves upon the shortcomings of the original models. This study reveals that mandating vaccination certificates for entry into a country is the most significant measure affecting airport performance. Additionally, aircraft movement at the airport has the greatest overall impact and can be considered the most crucial factor influencing airport performance from an operational standpoint. The findings show that both factors directly influence financial performance, as reflected in the net income. Some management implications are provided to mitigate the consequences of the measures taken to counter the pandemic crisis. This integrated approach should also assist authorities and policy-makers in planning cautious action for future crises.

Output feedback stabilization of stochastic high‐order nonlinear time‐delay systems with unknown output function

AbstractThis article considers the problem of output feedback stabilization for a class of stochastic high‐order nonlinear time‐delay systems with unknown output function. For stochastic high‐order nonlinear time‐delay systems, based on the Lyapunov stability theorem, by combining the addition of one power integrator and homogeneous domination method, the maximal open sector of output function is given. As long as output function belongs to any closed sector included in , an output feedback controller can be developed to guarantee the closed‐loop system globally asymptotically stable in probability.

Elevating sustainability with a multi-renewable hydrogen generation system empowered by machine learning and multi-objective optimization

The global energy landscape is rapidly shifting toward cleaner, lower-carbon electricity generation, necessitating a transition to alternate energy sources. Hydrogen, particularly green hydrogen, looks to be a significant solution for facilitating this transformation, as it is produced by water electrolysis with renewable energy sources such as solar irradiations, wind speed, and biomass residuals. Traditional energy systems are costly and produce energy slowly due to unpredictability in resource supply. To address this challenge, this work provides a novel technique that integrates a multi-renewable energy system using multi objective optimization algorithm to meets the machine learning-based forecasted load model. Several forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Random Forest and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), are assessed for develop the statistical metrics values such as RMSE, MAE, and MAPE. The selected Non-Sorting Moth Flame Optimization (NSMFO) algorithm demonstrates technological prowess in efficiently achieving global optimization, particularly when handling multiple objective functions. This integrated method shows enormous promise in technological, economic, and environmental terms, emphasizing its ability to promote energy sustainability targets.

Auxiliary diagnosis and prognosis evaluation of KIF4A, RAD51AP1 and CDKN3 in esophageal cancer

To investigate the expression of mRNA in esophageal cancer (ESCA) tissues and its potential and diagnostic and prognostic value by high-throughput sequencing data. Using the Cancer Genome Atlas Program (TCGA) database in USA by integrative bioinformatics analysis methods, the gene expression profiles and clinical data of 173 patients with ECSA were collected. The mRNA expression levels in ESCA tissue and para-cancerous tissue samples were analyzed using DESeq2, edgeR and limma to screen the differentially expressed genes (DEGs). DEGs-related protein network diagrams were drawn. GO and KEGG function enrichment analysis were performed and the hub genes were screened and the survival analysis of hub genes was analyzed. Genes related to the prognosis of ESCA were selected and their prognostic value in ESCA was analyzed. Finally, the receiver operating characteristic curve was drawn to evaluate its diagnostic value. The results showed that using TCGA cancer data, a total of 620 up-regulated DEGs and 668 down-regulated DEGs with significant differential expression between ESCA and para-cancerous tissues were screened. DEGs were mainly involved in receptor complexes, ubiquitin ligase complexes, etc., playing GTPase activity, phospholipid binding, and other molecular functions, and participating in the regulation of intracellular substance transport, small molecule metabolism, and other biological processes. Protein functional enrichment analysis showed that these proteins were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, Epstein-Barr virus infection, neutrophil extracellular trap formation, and other pathways involved in the formation and development process of ESCA. Survival analysis showed that the overall survival rate of ESCA patients with high expression of KIF4A, RAD51AP1, and CDKN3 was significantly shortened, and the difference was statistically significant (P<0.05). Furthermore, the areas under the curve (AUC) of KIF4A, RAD51AP1, and CDKN3 for diagnosing esophageal cancer were 0.956, 0.951 and 0.979, respectively, with sensitivities and specificities both exceeding 80%. Additionally, ROC results of the combined diagnostic model of these three genes showed an AUC of 0.979, with sensitivities and specificities of 0.914 and 1, respectively. This indicates that KIF4A, RAD51AP1 and CDKN3 have individual or combined auxiliary diagnostic value for ESCA. In conclusion, KIF4A, RAD51AP1 and CDKN3 have high diagnostic efficiency for ESCA, and their increased expression is closely related to the prognosis, suggesting that these three genes could be used as auxiliary diagnostic and prognostic factors for ESCA.

High-Temperature and High-Pressure Tribological Properties of Siliconized Graphite for Water-Lubricated Thrust Bearing Application in Main Coolant Pump

The effect of the microstructure of siliconized graphite on tribological properties is investigated by using a high-temperature and high-pressure water-lubricated tribometer on a self-mated ring-on-ring configuration under an applied load of 500–1500 N with a spindle speed of 100–5000 rpm in both 90 °C (5 MPa) and 25 °C (1 MPa) water environments, respectively. The Stribeck curves measurement and continuous wear tests are performed and analyzed in both water environments. The wear behaviors of the graphite, SiC, and free-silicon phases in siliconized graphite are demonstrated to explore the wear mechanism. The larger wear depths of a low-worn surface roughness on the three phases contribute to the boundary lubrication. The shallower wear depths are observed on the SiC and Si phases under the mixed lubrication, corresponding to partial contact wear of surface asperities. The wavy surface of the SiC phase and uniform flow-oriented striae of the Si phase are attributed to hydrodynamic lubrication, caused by full water film scouring the worn surface. Finally, an integrated evaluation method of G duty parameters is successfully used to identify the lubrication regimes of siliconized graphite from the boundary, mixed, to hydrodynamic lubrications for a water-lubricated thrust bearing application in the main coolant pump of a nuclear power plant.

Comparative analysis of left ventricle function and deformation imaging in short and long axis plane in cardiac magnetic resonance imaging

BackgroundAdvancements in cardiac imaging have revolutionized our understanding of ventricular contraction. While ejection fraction (EF) is still the gold standard parameter to assess left ventricle (LV) function, strain imaging (SI) has provided valuable insights into ventricular mechanics. The lack of an integrative method including SI parameters in a single, validated formula may limit its use. Our aim was to compare different methods for evaluating global circumferential strain (GCS) and their relationship with global longitudinal strain (GLS) and EF in CMR and how the different evaluations fit in the theoretical relationship between EF and global strain.MethodsRetrospective monocenter study. Inclusion of every patient who underwent a CMR during a 15 months period with various clinical indication (congenital heart defect, myocarditis, cardiomyopathy). A minimum of three LV long-axis planes and a stack of short-axis slices covering the LV using classical steady-state free precession cine sequences. A single assessment of GLS on long axis (LAX) slices and a double assessment of GCS and EF with both short axis (SAX) and LAX slices were made by a single experienced CMR investigator.ResultsGCS-SAX and GCS-LAX were correlated (r = 0.77, P &amp;lt; 0.001) without being interchangeable with a high reproducibility for GCS, GLS and EF. EF calculated from LAX images showed an overestimation compared to EF derived from SAX images of 7%. The correlation between calculated EF and theoretical EF derived from SI was high (r = 0.88 with EF-SAX, 0.95 with EF-LAX).Data conclusionThis study highlights the need to integrate strain imaging techniques into clinical by incorporating strain parameters into EF calculations, because it gives a deeper understanding of cardiac mechanics.

A BWM-TOPSIS Linear Programming Model for Evaluating the Performance of Health-Promoting Hospitals with McKinsey 7s Framework in Organizational Management

This research addresses the critical aspect of evaluating operational performance in health promotion hospitals, which play a vital role in providing medical services to local communities. The research proposes an integrated method for performance assessment, utilizing the Best-Worst Method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) linear programming model. Taking the case of health promotion hospitals in Maha Sarakham province, Thailand, and considering the McKinsey 7s framework's seven criteria, BWM is employed to determine the criteria weights. Subsequently, the TOPSIS linear programming model selects the ideal health promotion hospital based on these weights. The BWM analysis reveals criteria weights in the following order: system, staff, skill, style, structure, strategy, and shared value. The TOPSIS linear programming model identifies SH12 as the top-performing health promotion hospital with a closeness coefficient value of 0.8821. Additionally, a Spearman's rank correlation test validates this proposed method against the original TOPSIS approach, yielding a correlation value of 1.0. These findings provide valuable guidance for organizations, particularly in shaping strategic policies and resource allocation within medical service units, medical equipment, and personnel management in organizational settings. This study offers that the proposed method is simpler and will aid in the ongoing analysis of strengths and weaknesses in the improvement of organizations and development, helping organizations adapt to changes.

Feature fusion improves brain-interface paradigm based on steady state visual evoked potential blocking response

The steady-state visual evoked potential blocking response (SSVEP-BR) is produced on an electroencephalogram (EEG) when the SSVEP is interrupted or abolished and allows augmentation of brain-computer interface (BCI) paradigms. Integration of the SSVEP-BR with the SSVEP enables an increase in the number of commands without the addition of further stimuli but refinement would improve performance. The current study evaluated SSVEP-BR and a novel method to combine multiple features and enhance the performance of frequency recognition and SSVEP-BR identification is proposed. Correlation features were extracted by filter bank canonical correlation analysis (FBCCA) and synchronization features by multivariate synchronization index (MSI) before being integrated. The performance of correlation features extracted by task-related component analysis (TRCA) were also evaluated. The novel integrated method was compared with FBCCA, MSI and TRCA for performance in frequency recognition and SSVEP-BR identification. The novel integrated method achieved higher classification accuracy than FBCCA and MSI for benchmark datasets when the sliding window was used for EEG data. However, the accuracy of TRCA was not stable when the sliding window was used. The novel integrated method produced an improvement in SSVEP-BR identification over FBCCA and MSI in the blocking dataset. TRCA was not found to be effective for SSVEP-BR identification. A novel integrated method is proposed which gives higher classification accuracy and more stable performance than FBCCA, MSI and TRCA for frequency recognition and SSVEP-BR identification when the EEG data sliding window was used and shows superior performance for the BCI paradigm based on SSVEP and SSVEP-BR.

Consequence analysis of a small-scale hydrogen leakage from the overhead hydrogen piping based on machine learning and physical modeling

Leakage from the overhead hydrogen piping (OHP) must be contained because of the wide flammability range and low minimum ignition energy of hydrogen; rapid leakage detection and appropriate emergency responses are necessary to ensure safe OHP operation. Consequence analysis after leakage detection is useful for hazardous-area estimation and assists operators in emergency-response planning. However, consequence analysis and leakage-detection methods have not been combined so far, and an integrated method has not yet been developed. This study aimed to develop a method based on machine learning (ML), physical modeling, and consequence analyses for rapid and appropriate decision-making after hydrogen leakage. Initially, an OHP model was constructed using physical modeling; this is a method that models target systems based on fundamental physical equations. Subsequently, an ML model for leakage detection was constructed based on various pressure or flow-rate profiles obtained from the OHP model. Regression models were constructed to estimate the leakage diameter. In addition, consequence analyses were conducted to estimate the effects of hydrogen explosions and fires using the leakage diameters predicted by the regression model. We confirmed that the ML model could distinguish between leakage and non-leakage conditions, and the estimated consequences could be used to visualize the risk level of hazardous areas near the leakage points in the case studies. The results of the case studies demonstrated the effectiveness of the proposed method for rapid decision making when hydrogen leaks from OHP. This method enables improved emergency responses in post-leakage situations.

Investigation into the Methodology and Implementation of Life Cycle Engineering under China's Carbon Reduction Target in the Process Industry

The industrial sector is the primary source of carbon emissions in China. In pursuit of meeting its carbon reduction targets, China aims to promote resource consumption sustainability, reduce energy consumption, and achieve carbon neutrality within its processing industries. An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology. This strategy aims to attain an optimal solution for material performance, resource consumption, and environmental impact. In this study, five types of technologies were considered: raw material replacement, process reengineering, fuel replacement, energy recycling and reutilization, and material recycling and reutilization. The meaning, methodology, and development status of life cycle engineering technology abroad and domestically are discussed in detail. A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry, as an example. The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

Output Feedback Stabilization of High-Order Nonlinear Time-Delay Systems With Low-Order and High-Order Nonlinearities

In this paper, the output feedback stabilization of a class of high-order nonlinear time-delay systems with more general low-order and high-order nonlinearities is investigated. By constructing the new Lyapunov-Krasovskii functional and reduced-order observer, based on homogeneous domination theory together with the adding a power integrator method, an output feedback controller is developed to guarantee the equilibrium of the closed system globally uniformly asymptotically stable.

Bayesian simultaneous factorization and prediction using multi-omic data

Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended to simultaneously predict a continuous or binary phenotype while estimating latent factors, termed Bayesian Simultaneous Factorization and Prediction (BSFP). BSF and BSFP accommodate concurrent imputation, i.e., imputation during the model-fitting process, and full posterior inference for missing data, including “blockwise” missingness. It is shown via simulation that BSFP is competitive in recovering latent variation structure, and demonstrate the importance of accounting for uncertainty in the estimated factorization within the predictive model. The imputation performance of BSF is examined via simulation under missing-at-random and missing-not-at-random assumptions. Finally, BSFP is used to predict lung function based on the bronchoalveolar lavage metabolome and proteome from a study of HIV-associated obstructive lung disease, revealing multi-omic patterns related to lung function decline and a cluster of patients with obstructive lung disease driven by shared metabolomic and proteomic abundance patterns.

Development of future elementary school teachers' professional self-education readiness through interactive teaching methods

A modern specialist's ability to self-educate is a valuable professional quality. Education has been given the essential responsibility to develop the learners' readiness for self-education (RSE). However, university students experience difficulties organizing and implementing self-educational activities. The study's goal is to investigate the development of students' RSE and the impact of interactive training methods (ITMs) on ensuring the development of self-education skills in future elementary school (FES) teachers. The study used a pre- and post-test design for FES teachers' readiness for self-education (RSE). The study involved 240 learners at E. Buketov Karaganda University, Karaganda, Kazakhstan. The study found that university students experienced difficulties in organizing and implementing self-educational activities. The findings revealed that using integrative teaching methods increased students' readiness for self-education which is a significant improvement over traditional methods. This paper contributes to the methodological potential of university education by providing an innovative approach to improve university students' self-education preparation.

PERSONNEL CERTIFICATION AS A COMPANY DEVELOPMENT STRATEGY

The purpose of this article is to explore how personnel certification serves as a strategic tool for organisational development. It examines the importance of certifying employees as a means of enhancing organisational capability and competitiveness. By examining the role of certification in improving employee skills and knowledge, the article sheds light on its impact on organisational performance and growth. It also discusses how certified personnel contribute to building a skilled workforce, fostering innovation and meeting industry standards. Overall, the article highlights the importance of personnel certification as a proactive approach to advancing organisational development goals. The object of the study is the process of personnel certification and its impact on the company's development. Methodology. The use of an integrated method helps to eliminate formalism and subjectivity in personnel certification, increase openness and objectivity in the assessment, and stabilise the social and psychological climate in the team. When applying this method, a prerequisite is the unconditional support of the first manager, who should lead the personnel certification and implement its results through the HR services. Without it, certification will be formal and will not yield positive results. The comprehensive method of personnel evaluation integrates various assessment techniques to provide a holistic understanding of an individual's skills, competencies and performance within an organisation. It takes into account multiple factors such as job performance, behavioural characteristics and growth potential, allowing for a thorough analysis of an employee's contribution to the organisation. The comprehensive method of personnel evaluation is based on the use of groups of methods: descriptive method (biographical method; method of oral and written assessments, method of group discussions, method of standards, matrix method and method of free and compulsory choice of assessment situations); combined methods are methods based on the descriptive principle, but formalised with the help of conventional quantitative indicators (sum of scores method, method of specified groupings, testing). Research results. Evaluating personnel performance helps to attract and retain highly skilled and motivated employees, while analysing the effectiveness of recruitment strategies and methods will help to attract talented employees. Practical implications. The use of personnel evaluation methods will allow to understand how personnel certification contributes to creating a positive working environment and increasing the level of employee satisfaction; to study the impact of personnel certification strategies on the company's reputation among employees; to formulate specific recommendations for practical managers and personnel specialists for the successful implementation of personnel certification strategy; to develop strategic solutions aimed at increasing the competitiveness and stability of the company. Value/Originality. Personnel certification helps to improve internal and external communication, which contributes to a more effective exchange of information and helps to maintain unity among employees; it allows companies to adapt to changes in the labour market, creating attractive conditions for employees in new conditions. In general, effective personnel certification is determined by the ability not only to attract but also to retain talented employees, thus contributing to the sustainable development of the company in a competitive business environment.

Integrative Analysis of Site-Specific Parameters with Nuisance Parameters on the Common Support

AbstractHigh-throughput technologies in bioscience have pushed us into an era with high dimensionality. Swamped by thousands of predictors, detecting the valuable signal from the noise in clinical studies becomes challenging. As a common strategy, integrative analysis utilizing similarities across multiple studies might help lift the curse of dimensionality and enhance statistical power. However, due to the growing concern about individual data privacy, data-sharing constraints are often imposed in integrative analysis. These might lead to results inequivalent to ones without sharing constraints and reduce statistical power in integrative analyses. In this paper, built on Abess, we propose an integrative analysis method to estimate the site-specific parameters in the presence of high dimensional nuisance parameters in multi-site studies. Implemented with a carefully designed $$L_{2,0}$$ L 2 , 0 penalization on nuisance parameters, the proposed method satisfies both the DataSHIELD constraint, which only allows the transmission of summary statistics from sites, and the equivalence property that the solution is exactly the same as the solution merging all datasets into one on a single location. Assuming the nuisance parameters share a common support, the proposed method has support recovery and selection consistency with high probability and exhibits improved estimation accuracy on the site-specific parameters and low computational cost in numerical experiments. We demonstrate the merit of the proposed method by investigating the relationship between the CD8 T cell count and the treatment effect of zidovudine-incorporated therapy in the AIDS Clinical Trials Group Study 175.

A new mechanism for crisis management success

AbstractThe goal of this study is to present a thorough analysis of the ways that organisational culture (OC) and risk assessment competencies of leaders affect the creation of strategic frameworks, especially about high‐quality crisis decision‐making. A self‐administered questionnaire was distributed to 436 organisational personnel; 304 valid responses were received, and these were analysed using variance‐based structural equation modelling. The findings show that OC, leadership risk assessment and a strategic management plan (SMP) are favourable predictors of high‐quality crisis decision‐making. According to this research, every variable strengthens the link between SMP quality and crisis decisions. The study is supported by an integrated method for understanding complex organisational facts. Practical ramifications for leadership development include strengthening leaders' capacity to appraise risk, considering OC and employing an SMP to guarantee that, when making sound crisis judgements, they are both innovative and compliant with organisational ethics.

Comparison and Analysis of the Effectiveness of Linear Regression, Decision Tree, and Random Forest Models for Health Insurance Premium Forecasting

Health insurance is a type of insurance that covers individual and family medical expenses and is important for the health and financial security of individuals and families. To better predict the demand for health insurance, three regression models in machine learning - random forest, linear regression, and decision tree - are widely used for health insurance prediction. Among these three regression models, random forest regression has the best prediction effect with a model score of 0.8564, which is the best prediction effect among the three models. Random forest regression is an integrated learning method that combines multiple decision tree models into a more powerful model that can effectively avoid overfitting problems and can handle large amounts of data. Therefore, random forest regression is a very effective method for health insurance prediction. The next model is the linear regression model with a model score of 0.7584. The linear regression model is a basic regression model that can be used to predict a linear relationship between two variables. In health insurance prediction, linear regression modeling can be used to predict the linear relationship between health insurance costs and related factors such as age, gender, and illness. The worst predictor was the decision tree, with a model score of 0.7097. The decision tree model can be used in Medicare forecasting to predict nonlinear relationships between Medicare costs and related factors such as age, gender, and illness.