Predictive Power Research Articles

ENTREPRENEURIAL ORIENTATION AND SME SUCCESS: UNVEILING THE MEDIATING IMPACT OF AMBIDEXTERITY

This research seeks to assess how ambidexterity in exploration and exploitation mediates the relationship between entrepreneurial orientation and the performance of small and medium-sized enterprises (SMEs). Specifically, this study investigates the indirect influence of entrepreneurial orientation on business performance through the mediating roles of ambidexterity exploration and exploitation. A quantitative approach using structural equation modeling (SEM) is employed to examine the relationships among these variables. The research sample comprises 200 SMEs operating primarily in East Java, Indonesia. The findings demonstrate a significant mediating effect (p<0.05) of ambidexterity exploration and exploitation, which enhances the influence of entrepreneurial orientation on business performance. This indirect effect proves to be stronger than the direct impact of entrepreneurial orientation itself. The structural model shows a high R-square value of 68.6%, indicating a strong predictive capability. Additionally, the goodness-of-fit index reflects high model suitability (0.543 > 0.36), and the predictive power of the PLS model is considered medium. The study’s results underscore the importance of fostering both ambidexterity in exploration and exploitation alongside entrepreneurial orientation to optimize SME performance.

Evaluation of the GATIS score for predicting prognosis in rectal neuroendocrine neoplasms

The GATIS score, developed by Zeng et al , represents a significant advancement in predicting the prognosis of patients with rectal neuroendocrine neoplasms (R-NENs). This study, which included 1408 patients from 17 major medical centres in China over 12 years, introduces a novel prognostic model based on the tumour grade, T stage, tumour size, age, and the prognostic nutritional index. Compared with traditional methods such as the World Health Organization classification and TNM staging systems, the GATIS score has superior predictive power for overall survival and progression-free survival. With a C-index of 0.915 in the training set and 0.812 in the external validation set, the GATIS score’s robustness and reliability are evident. The study’s use of a large, multi-centre cohort and rigorous validation processes underscore its significance. The GATIS score offers clinicians a powerful tool to accurately predict patient outcomes, guide treatment decisions, and improve follow-up strategies. This development represents a crucial step forwards in the management of R-NENs, addressing the complexity and variability of these tumours and setting a new benchmark for future research and clinical practice.

Capital mobility, financial development and sustainable trade development in sub-Saharan Africa: a GMM-PVAR based analysis

PurposeThe existing literature on the effects of capital mobility and financial development on sustainable trade development in sub-Saharan African (SSA) countries has been centered on production-based carbon emissions without investigating consumption-based or trade-adjusted carbon emissions. The purpose of this paper is to examine the effects of capital mobility and financial development on sustainable trade development, specifically trade-adjusted carbon emissions in SSA economies.Design/methodology/approachThe study employed the novel GMM-PVAR estimator and the Drisc-Kraay fixed effect panel corrected standard error (PCSE) dynamic ordinary least squares (DOLS) and the fully modified least squares (FMOLS) approaches on panel data from 46 sub-Saharan African (SSA) countries over the period 1992–2021.FindingsThe study established that capital mobility has a significant positive effect on sustainable trade development in SSA in the long run. Further, the empirical results reveal that the link between financial development and sustainable trade development is significantly positive in the long run. Moreover, the results suggest that capital mobility and financial development have predictive power on sustainable trade development.Practical implicationsThe findings of the study imply that policymakers ought to pay equal attention to capital mobility and financial development when developing sustainable trade development policies.Originality/valueThe existing literature has been centered on production-based carbon emissions, without specifically considering sustainable trade development (consumption-based carbon emissions). To the best of our knowledge, this is the first study to examine the effect of capital mobility and financial development on sustainable trade development in SSA countries context.

Beyond Competitiveness: Is Productivity Everything?

ABSTRACTThis study examines and compares the predictive power of competitiveness, approximated by the Global Competitiveness Index (GCI), and productivity for economic growth. Using a panel dataset of 62 countries over the period 2007–2019 and the local projections (LP) approach, we find that both higher productivity and higher GCI contribute to higher economic growth over the next 5 years. However, productivity has a stronger predictive power for economic growth than the GCI. We find evidence that this divergence between productivity and GCI could be explained by ‘productivity beyond competitiveness’: As economies develop and converge towards the world technological frontier, the GCI, unlike productivity, no longer captures the key determinants of economic growth. We then examine the determinants of ‘productivity beyond competitiveness’ that can help economies increase their productivity, and by extension growth, even as they approach the technological frontier. We find that higher financial development, institutional quality, as well as some cultural traits explain the divergence between productivity and competitiveness in advanced economies.

Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma

Objectives The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Materials and Methods Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36). Results Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI. Conclusions CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.

EGS SO13 - Evaluation of Different Surgical Risk Prediction Models in Predicting Morbidity and Mortality in Giant Hiatus and Diaphragmatic Hernia Repairs

Abstract Background Emergency giant hiatus hernia and diaphragmatic hernia repairs are associated with high rates of mortality and morbidity; different studies reported variable outcomes. Accurate risk assessment empowers patients to provide informed consent and also serves as a valuable tool for identifying individuals who may require enhanced postoperative care, such as respiratory support and critical care admission. Various risk prediction tools have been developed to assist clinicians in making decisions to aid in stratifying the risk of surgical management. We aimed to assess the effectiveness of different risk prediction models in predicting mortality and morbidity in this patient cohort. Method Retrospective analysis performed on cases between May, 2010 and January, 2021 in a tertiary adult upper gastro-intestinal centre. The outcomes from the operations were assessed and compared with the predicted morbidity and mortality for different risk prediction models. Including the Surgical Outcome Risk Tool (SORT), the National Emergency Laparotomy Audit (NELA), parsimonious NELA and the American College of Surgeons National Surgical Quality Improvement Programme (ACS-NSQIP). The models were compared using the area under the curve (AUC). Morbidity models included Portsmouth Physiological and Operative Severity Score (P-POSSUM) and the ACS-NSQIP. Morbidity was evaluated by calculating the comprehensive complication index (CCI). Results A total of 108 patients met the criteria and were included in the analysis. Among these 50.9% were females. The median age was 69 (IQR 59-78). The 30-day mortality rate was 6.93% (7 patients), six patients died during the same admission, and one patient died during a re-admission. ACS-NQSIP had the highest predictive power for mortality (AUC = 0.6344), in comparison to NELA (AUC=0.5665), parsimonious NELA (AUC=0.4866) and SORT (AUC = 0.4342). Both ACS-NSQIP and P-POSSUM showed moderate correlation to CCI (rho = 0.489, p<0.001 and 0.446, p<0.001 respectively). Conclusion ACS-NSQIP is a better predictor of both mortality and morbidity in emergency giant hiatus and diaphragmatic hernia repairs when compared to NELA, parsimonious NELA, P-POSSUM and SORT. Interestingly, parsimonious NELA had a lower prediction rate when compared with the older NELA model. ACS-NSQIP may have a role in pre-assessment and consenting of emergency hiatus and diaphragmatic hernia repairs. Multi-centre prospective studies could be used to validate these findings.

The return predictability of technological links: evidence from the Chinese STAR Market

ABSTRACT This paper examines the predictive power of technology-linked returns in the Chinese Science and Technology Innovation Market (STAR Market). Using a sample from August 2019 to June 2023, we construct technology-linked returns based on patent portfolio similarities among firms. Our findings indicate that these returns significantly predict future stock returns, with a long-short strategy yielding weekly excess returns between 0.50% and 0.53%. Analysis of trade sizes reveals that institutional investors are more likely to drive this predictability, as the direction of large trades aligns with technology-linked returns. This study explores how technology-peer information affects stock returns in an emerging, technology-intensive market, highlighting the significant role of traders with large capital in leveraging technology-peer information.

Predictive Powers of Carbon Emissions and Oil Price on Stock and Foreign Exchange (FX) Markets Using Multivariate Recurrent Neural Networks (RNN) Model

The predictive power of environmental factors such as CO2 levels and Brent oil price was evaluated on the stock market (S&P 500) and foreign exchange (FX) rates for market currency pairs (EUR/USD) and emerging market currency pairs (MXN/USD) with recurrent neural networks (RNN). The S&P 500 is a collection of well-established companies in the United States representing the macroeconomic health of the nation and the global economy. The FX market is a global decentralized over-the-counter (OTC) market used to determine the spot price of currency pairs. A highly leveraged market usually trades within a specific price range. Since stocks and FX pairs are highly correlated to macroeconomic factors, it was hypothesized that environmental factors such as CO2 levels and oil prices also have predictive power due to their close causal relationship with anthropological economic activities. To verify the predictive power, an RNN model was built, and a bi-directional neural network with an internal state was used to process data sequences. The performance of RNN was quantified by measuring the residual prediction from the true value. Although the study at its current state might need further statistical rigor, it concluded that environmental factors increased the predictive power for the S&P 500 while decreasing it for the DM FX pair and the EM FX pair showed mixed results.

Introducing GWAStic: A user-friendly, cross-platform solution for genome-wide association studies and genomic prediction

Abstract Motivation Advances in genomics have created an insistent need for accessible tools that simplify complex genetic data analysis, enabling researchers across fields to harness the power of genome-wide association studies and genomic prediction. GWAStic was developed to bridge this gap, providing an intuitive platform that combines artificial intelligence with traditional statistical methods, making sophisticated genomic analysis accessible without requiring deep expertise in statistical software. Results We present GWAStic, an intuitive, cross-platform desktop application designed to streamline genome-wide association studies and genomic prediction for biological and medical researchers. With a user-friendly graphical interface, GWAStic integrates machine learning and traditional statistical approaches to support genetic analysis. The application accepts inputs from standard text-based Variant Call Formats and PLINK binary files, generating clear graphical outputs, including Manhattan plots, quantile-quantile plots, and genomic prediction correlation plots, to enhance data visualization and analysis. Availability and implementation Project page: https://github.com/snowformatics/gwastic_desktop GWAStic documentation: https://snowformatics.gitbook.io/product-docs PyPI: https://pypi.org/project/gwastic-desktop/

Abstract 4138446: Ceramides Surpass LDL-Cholesterol in Predicting Cardiovascular Mortality in Diabetic and Non-Diabetic Patients

The power of cardiovascular risk prediction is biased by comorbidities (including T2DM), medical treatment, and age, which impact cholesterol levels. Consequently, when applying LDL-C, the true cardiovascular risk of such confounded patients is often underestimated. However, it is not clear whether this constraint for risk prediction may likewise apply to other lipid risk markers in particular ceramides. In this observational cohort study, we recorded cardiovascular mortality for 16 years in 1195 patients. Their median age was 67 years, all of them had a high preexisting cardiovascular risk, 51% were taking statins, and 30% had T2DM. We found a U-shaped association between cardiovascular mortality and LDL-C. We thus stratified the population according to the vertex into high (≥150mg/dL) and lower LDL-C (<150mg/dL) and also according to the T2DM status. A Cox regression analysis revealed that LDL-C failed to predict cardiovascular mortality in any subgroup. In contrast, no U-shaped but a linear association was found for ceramide-based markers. They were able to significantly predict cardiovascular mortality after stratification and even after multivariate adjustment. We thus propose that ceramide-based predictors rather than LDL-C should be applied for a more accurate cardiovascular risk prediction in diabetic and other high-risk patients.

Multisensory Integration in Lexical Processing: Predicting Word Recognition Through Cross-Modal Capability

The integration of multisensory in lexical processing has garnered significant interest in cognitive linguistics and psycholinguistics. This study investigates the predictive power of cross-modal capability (CMC) on word recognition, focusing on how sensory integration influences cognitive processing and lexical decision-making. By collecting five-sense ratings for synesthetic adjectives, we computed the cross-modal capability for each word. We then conducted regression analyses to examine the relationship between these indices and reaction time in lexical decision tasks. Our findings reveal that words with higher cross-modal capabilities, as measured by CMC, are recognized and processed more quickly. This superior predictive power of CMC underscores the importance of sensory associations in lexical processing and highlights the need for comprehensive indices that account for multiple sensory inputs. These results provide deeper insights into the cognitive mechanisms underlying word recognition and processing speed, suggesting that CMC is a better independent variable for partially predict lexical cognition and recognition. This research contributes to our understanding of multisensory integration in lexical processing and has implications for models of word recognition and cognitive linguistics.

A Novel Preoperative Classification System for Selecting Suitable Surgeries in Liver Transplant Patients with Portal Vein Cavernous Transformation

Background To evaluate the new preoperative Changgung classification (CC) system of portal vein thrombosis (PVT) in choosing suitable operative procedures to reconstruct portal veins during liver transplantation (LT) in patients with portal vein cavernous transformation (PVCT). Methods This retrospective observational study analyzed data from allograft LTs performed for various liver diseases Results The study included 22 males and 4 females with LT indications comprising cirrhosis (n = 9), hepatocellular carcinoma (n = 12), PVCT (n = 2), liver failure from fulminant hepatitis B (n = 1), dysfunction of transplanted liver (n = 1), and chronic rejection of transplanted liver (n = 1). Patients were classified according to Yerdel (21 Yerdel II and 5 Yerdel III) and CC (C1–C5). In total 16 simple operations were performed on C1–C3 cases and 9 complex operations on C4–C5 cases, with one additional simple operation. The distribution according to the Yerdel classification was 16 simple and 5 complex operations in Yerdel II cases and 1 simple and 4 complex operations in Yerdel III cases. The median follow-up time was 27.5 months with overall one-year and three-year OS rates of 88.1% and 83.9% for the cohort. Specifically, the one-year OS rates for patients classified as C1-3 vs. C4-5 were 93.3% and 80.0%, while the three-year OS rates were 86.7% and 80.0%, respectively (p = 0.526). Conclusion The CC proposed in this study shows comparable potential to the Yerdel classification in preoperatively identifying the need for complex surgical techniques in LT patients with PVCT and may also have predictive power for the survival benefits following LT.

Abstract 4130072: Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging

Aims: The significance of cardio-renal interactions in heart failure (HF) prognosis has become increasingly evident, yet there are no established methods to assess them. To address this issue, we propose a novel approach using Superb Microvascular Imaging (SMI), an ultrasound method that that enables detailed visualization of microvascular flow, to assess renal microcirculation. Methods: We retrospectively analyzed 78 patients who underwent renal ultrasonography using SMI from October 2020 to May 2023. We measured changes over time of Vascular Index (VI), which quantifies the blood flow signal area in the region of interest on the SMI image (Figure 1). Key measurements included Maximum VI (Max.VI), Minimum VI (Min.VI), and the cyclic variation of VI, calculated as the intrarenal perfusion index (IRPI) = (Max.VI - Min.VI) / Max.VI within one cardiac cycle. The primary endpoint of this study was a composite event (CE), defined as a composite of all-cause death and unplanned hospitalization for worsening HF. Results: During a mean follow-up period of 1.6±0.8 years, 13 of 78 patients (17%) experienced a CE. Compared with patients without CE, those with CE had significantly lower Max.VI (0.28±0.15 vs 0.46±0.18, p=0.002) and Min.VI (0.10±0.12 vs 0.22±0.14, p=0.007), and IRPI was significantly higher in the event group (0.68±0.19 vs 0.55±0.17, p=0.018). In univariable Cox regression analyses, Max.VI (HR 0.35, 95%CI 0.17-0.71, p=0.004), Min.VI (HR 0.28, 95%CI 0.10-0.75, p=0.012), IRPI (HR 1.97, 95%CI 1.11-3.51, p=0.021), creatinine (Cr) (HR 1.67, 95%CI 1.26-2.21, p<0.001) and estimated right atrial pressure by echocardiography (eRAP) (HR 4.22, 95%CI 1.42-12.5, p<0.001) were significantly associated with CE. In multivariable Cox regression analyses, Min.VI remained significantly associated with CE in the model adjusted for Cr (HR 0.35, 95%CI: 0.13-0.92, p=0.033), and in the model adjusted for eRAP (HR 0.11, 95%CI: 0.11-0.82, p=0.019). ROC curve analysis showed that, although not statistically significant, the predictive power of Min.VI was superior to that of Cr and eRAP (Figure 2). Stratifying patients by a Min.VI cut-off value of 0.08, determined through ROC analysis, showed that those with Min.VI < 0.08 had a significantly poorer prognosis (log-rank p<0.001) (Figure 3). Conclusion: The use of Superb Microvascular Imaging to assess renal circulation provides new insights into the prognosis of HF and allows risk stratification beyond conventional markers.

Abstract 4136932: Impact of Different Socioeconomic Metrics on Heart Failure-Related Admission and Short-Term Outcomes in Maryland

Introduction: Annually, over 500,000 Americans are hospitalized due to heart failure (HF), marking it as a major contributor to morbidity and mortality. It also poses a significant financial burden and leads to considerable losses in productivity. Objective: This study investigates the predictive accuracy of different socioeconomic metrics on the risk and outcomes of HF in Maryland. Methodology: A retrospective analysis of the Maryland State Inpatient Database (2016-2020) was conducted to assess the predictive accuracy of race/ethnicity, insurance status, household median income, and neighborhood poverty level (measured by the Distressed Communities Index) on the risk of heart failure-related hospital admissions and outcomes. Multivariate logistic regression models were also used to adjust for confounders. Result: During the study period, a total of 389,220 cases of HF were reported in the Maryland SID. The majority of these patients were white (56.8%) and female (51.1%), with a median age of 73 years (interquartile range [IQR] 62-82 years). The in-hospital mortality rate was 5.1%, while rates of atrial fibrillation, cardiac arrest and prolonged hospital stay were 34.4%, 0.3%, and 48.4%, respectively. Multivariate analysis revealed a substantial area under the ROC curve (AUC) indicating good model performance: 0.88 for predicting HF, 0.64 for atrial fibrillation, 0.64 for cardiac arrest 0.57 for prolonged hospital stays, 0.63 for mortality. Subgroup analyses showed variable predictiveness by race (AUC = 0.4378), payment method (AUC = 0.5754), income quartile (AUC = 0.5202), and deprivation composite score (AUC = 0.4751). Patients with private insurance had the highest risk of stress cardiomyopathy (odds ratio [OR] = 1.98; 95% confidence interval [CI] 1.70-2.29). Socioeconomic metrics, including neighborhood distress, showed varying predictive accuracy for the HF-related admissions and selected short-term outcomes, with the highest predictive accuracy for neighborhood distress on the risk of HF (AUC = 0.50, std: 0.006), atrial fibrillation (AUC = 0.48, std: 0.0007), cardiac arrest (AUC = 0.51, std: 0.007), and prolonged hospital stays (AUC = 0.53, std: 0.0005) and mortality (AUC = 0.50, std: 0.0015). Conclusion: Neighborhood poverty level demonstrates significant predictive power for assessing the risk of HF-related hospital admissions and the short-term outcomes among Maryland residents, exceeding factors like insurance and race/ethnicity.

Research progress on the development of myopia prediction models and their predictive performance

The incidence of myopia is continuously increasing worldwide, especially in China, where the prevention and control of myopia is of great significance. Accurate prediction of myopia and early intervention are of great importance for slowing down the development of myopia and reducing the social burden. This article reviews the research progress of myopia prediction models and their predictive performance in recent years. It includes models based on refractive power and ocular biological characteristics, such as those using parameters such as refractive power, axial length, and percentile curves for prediction; models combined with environmental factors, which explore the relationship between myopia in school-age children and the age of onset, parents' myopia, education, and various living environmental factors; models based on genetic factors, which predict by evaluating parents' myopia status and single nucleotide polymorphisms discovered in genome-wide association studies. In addition, the article also introduces the application of artificial intelligence in myopia prediction, such as machine learning and deep learning algorithms that can predict changes in axial length growth and refractive power. These research advances provide a reference for establishing more accurate myopia prediction models and help achieve precise and personalized myopia prevention and control.

Diagnostics and Prognostics with High Dimensional Spatial-Temporal Data: From Structures to Human Brains

Diagnostics and prognostics with high-dimensional spatial-temporal data require innovative methodologies due to the inherent complexity of such datasets. This thesis explores the challenges of diagnostics and prognostics in high-dimensional spatial-temporal data, extending from physical structures to complex human brain analyses through resting-state functional magnetic resonance imaging (rs-fMRI). Drawing an analogy to engineering structural health monitoring using spatial-temporal vibration data, the approach leverages techniques from engineering diagnostics and prognostics data analytics to handle clinical problems with similar characteristics. A pioneering approach is developed to analyze multimodal datasets that not only include advanced rs-fMRI features—Amplitude of Low-frequency Fluctuations (ALFF), Regional Homogeneity (ReHo), Euler Characteristics (EC), and Fractal Analysis—but also encompass a wide array of clinical data. This integration includes infant developmental metrics such as birth weight and gestational age, maternal health factors like BMI and fat mass, and environmental influences including dietary intake and mental health during pregnancy. The study establishes a robust computational framework that uses advanced machine learning algorithms to analyze the interplay of these diverse data types, enhancing the precision and predictive power of our models for early childhood development. Initial validations have demonstrated the effectiveness of this comprehensive approach in identifying ADHD, with ongoing efforts aimed at expanding the methodology to address a broader range of developmental disorders. This work not only advances the diagnostic and prognostic capabilities in medical imaging but also significantly contributes to the field of Prognostics and Health Management (PHM). By providing a solid foundation for managing and understanding high-dimensional and multimodal spatial-temporal data across various disciplines, it bridges the gap between engineering and clinical diagnostics, demonstrating the potential for cross-disciplinary innovation.

Large quarterly Bayesian vector autoregression model for the modern Russian economy

It is often difficult to choose the necessary variables when we are forecasting the economic dynamics. On the one hand, vector autoregression (VAR) models can solve this problem by taking into account a sufficiently large number of variables. However, on the other hand, excessive parametrization of such models is not always justified, because it is often possible to choose such a small combination of variables the predictive power of which will be no worse than unlimited VARs with a large number of variables and lags. Bayesian methods help to solve this problem by introducing a priori restrictions on the VAR coefficients. The main goal of this work is to build a Large quarterly Bayesian vector autoregression (QBVAR) for the modern Russian economy. The hypotheses of the study: 1) BVAR(p) models reveal their maximum potential with a small number of variables, but with a large number of lags, 2) The Minnesota prior distribution is not always the best option for the modern Russian economy in the BVAR(p) model. As a result of the analysis, it turned out that for a number of the most important macroeconomic variables (GDP, inflation, key rate, unemployment, wages), Normal-Flat / Wishart-type priors turned out to be the optimal. In case of 29 input variables and two lags, the predictive power of the QBVAR(2) model is worse than the equivalent frequency VAR(2) or VECM(2). However, when it was possible to find the optimal combinations of variables, QBVAR(p) turned to be several times more accurate than its analogous frequency VAR(p) for all important macro indicators. In addition, the stiffness parameters of the considered a priori distributions decreases with increasing lags entering the model, i. e. the higher the order of the model with the minimum optimal set of variables, the tighter the distribution is necessary to obtain more accurate predictions. For the Russian realities, it is necessary to use very tight priors, 6–8 variables and 9–12 lags in a quarterly representation.

Effects of physical activity on depression and physical fitness among Shanghai junior high school students

This study explores the relationship between light physical activity (LPA) and moderate-vigorous physical activity (MVPA), depression and physical fitness among Shanghai junior high school students. A nationwide offline survey was conducted in Shanghai between August and December 2023 using a purposive sampling design. A total of 432 questionnaires were administered to six seventh-grade classes and eighth-grade classes from six schools in Shanghai; 416 responses were included in the final analysis. SPSS software version 29, the Correlation and Multiple Linear Regression techniques were employed to analyze the collected data. Higher levels of LPA and MVPA were correlated with lower levels of depression. However, only LPA was a significant positive predictor for depression among Shanghai junior high school students. MVPA did not show significant predictive power for depression. Only MVPA was positively correlated with 50 meters (50M), standing long jump (SLJ), 800/1000 meters (800/1000M), and total physical fitness (TPF), but not for sitting forward bend (FB) and vital capacity (VC); various measures of physical fitness were found highly correlated with each other. MVPA was also a significant positive predictor for 800/1000M, SLJ, 50M and TPF but not for VC and FB. LPA did not show significant predictive power for any physical fitness measures. The findings highlighted the importance of MVPA in enhancing physical fitness outcomes, while LPA primarily impacted depression levels. Various measures of physical fitness were highly interrelated. The results underscored the significance of promoting MVPA to improve physical fitness and reduce depressive symptoms in this population.

Machine Learning-Based Risk Prediction of Asthma

This paper will attempt to detect asthma early based on machine learning. This work utilizes a dataset consisting of 21 features such as family history of asthma, intake of drugs, smoking status, and quality of air and much more. The work tries to develop models with maximum precision, recall, and accuracy in asthma classification. These include several machine learning algorithms evaluated: SVM, Decision Trees, and Random Forest. This study focuses on both medical and environmental data when used in combination. The results here show that the Random Forest model performed well in achieving an overall high performance, with an accuracy of 0.926829. This has dire implications for evaluation and early diagnosis of Asthma. Key Words: Asthma detection, Machine learning, Patient history, Environmental factors, Support Vector Machines (SVM), Decision Trees, Random Forests, Predictive models, Air quality, Smoking habits, Medication usage, Family asthma history, Ac- curacy, Precision, Recall, Early diagnosis, Healthcare, Medical data, Environmental data, Predictive power

Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.