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Identified or conflicted: a latent class and regression tree analysis explaining how identity constructs cluster within smokers

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BMC PSYCHOLOGY 2022年第1期10卷 1页

作者： Meijer, E. Gebhardt, W. A. van Laar, C. Chavannes, N. H. van den Putte, B. Leiden Univ Med Ctr Publ Hlth & Primary Care Leiden Netherlands Leiden Univ Med Ctr Natl eHlth Living Lab Leiden Netherlands Leiden Univ Hlth Med & Neuropsychol Leiden Netherlands Univ Leuven Social & Cultural Psychol Leuven Belgium Univ Amsterdam Amsterdam Sch Commun Res Amsterdam Netherlands

Identity, or 'who I am', is important for smoking behaviour. Identity constructs (parts of a person's identity) are typically examined as separate entities, but emerging evidence suggests that the multifaceted nature of identity is relevant in the context of smoking. This cross-sectional study examined how smoking-related self- and group-identity constructs cluster within adult daily smokers (N = 231), whether classes of smokers can be distinguished based on clusters of identity constructs, and which factors explain class membership. Data were collected online in The Netherlands and Belgium, 2017-2018. Latent class and regression tree analyses showed that participants in Class 1 of 'Identified smokers' (estimated population share 54%) reported stronger smoker self- and group-identities, stronger expected identity loss when quitting smoking, and weaker quitter self-identities and non-smoker self- and group-identities (vs. Class 2 of 'Conflicted smokers'). Class membership was explained by the interaction between mental smoking dependence (dominant explanatory variable), consideration of future consequences, age of smoking onset, self-efficacy, and future self thought clarity. Models had good fit. The identity of more dependent smokers is more strongly oriented toward smoking. Smoking is also more strongly embedded in the identity of smokers who started smoking young, are less inclined to think about the future, and have lower self-efficacy.

关键词： Smoking Identity Latent class analysis regression tree analysis

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Association Between Physical Function and Neighborhood Environment in Healthy, Older Adults: An Exploratory Study Using regression tree analysis

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GERONTOLOGY AND GERIATRIC MEDICINE 2021年 7卷 23337214211052403页

作者： Ando, Masataka Kamide, Naoto Shiba, Yoshitaka Sakamoto, Miki Sato, Haruhiko Murakami, Takeshi Watanabe, Shuichiro Kitasato Univ Sch Allied Hlth Sci Sagamihara Kanagawa Japan Kitasato Univ Grad Sch Med Sci Sagamihara Kanagawa Japan Fukushima Med Univ Sch Hlth Sci Fukushima Japan Kansai Med Univ Fac Rehabil Hirakata Osaka Japan JF Oberlin Univ Grad Sch Machida Tokyo Japan

The aim of this study was to perform an exploratory investigation of the individual characteristics of older adults that affect the relationships between physical function and neighborhood environment. A total of 624 community-dwelling older adults living independently, aged >= 65 years, participated in this cross-sectional study. Physical function was assessed by muscle strength (grip strength and knee extension strength) and physical performance (5-m walking time and Timed Up and Go Test). The neighborhood environment was assessed using the International Physical Activity Questionnaire Environmental Module. The individual characteristics that affect the association between both were analyzed using multiple regression analysis and Classification and regression tree (CaRT) analysis. In both older men and women, multiple regression analysis showed that neighborhood environment was significantly associated with physical function. On the other hand, on CaRT analyses, older men <= 80 years of age without low back pain and depressive symptoms and perceived good access to recreational facilities had the shortest 5-m walking time. However, CaRT analyses found no relationship between physical function and neighborhood environment in older women. The relationships between physical function and neighborhood environment may be altered by sex, age, and physical and mental health conditions.

关键词： community-dwelling older adults physical function neighborhood environment regression tree analysis

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regression tree analysis of ecological momentary assessment data

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HEALTH PSYCHOLOGY REVIEW 2017年第3期11卷 235-241页

作者： Richardson, Ben Fuller-Tyszkiewicz, Matthew O'Donnell, Renee Ling, Mathew Staiger, Petra K. Deakin Univ Sch Psychol eMental Hlth Unit Burwood Australia

An increasingly popular form of data collection in health psychology research is Ecological Momentary Assessment (EMA);that is, using diaries or smartphones to collect intensive longitudinal data. This method is increasingly applied to the study of relationships between state-based aspects of individuals' functioning and health outcomes (e.g., binge eating, alcohol use). analysis of such data is challenging and regression tree modelling (RTM) may be a useful alternative to multilevel modelling for investigating the association between a set of explanatory variables and a continuous outcome. Furthermore, RTM outputs decision trees' that could be used by health practitioners to guide assessment and tailor intervention. In contrast to regression, RTM is able to easily accommodate many complex, higher-order interactions between predictor variables (without the need to create explicit interaction terms). These benefits make the technique useful for those interested in monitoring and intervening upon health and psychological outcomes (e.g., mood, eating behaviour, risky alcohol use, and treatment adherence). Using real data, this paper demonstrates both the benefits and limitations of RTM and how to extend these models to accommodate analysis of nested data;that is, data that arise from EMA where repeated observations are nested within individuals.

关键词： regression tree analysis ecological momentary assessment experience sampling tailored intervention decision tree

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Vehicle-Group-Based Crash Risk Prediction and Interpretation on Highways

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 7807-7818页

作者： Zhu, Tianheng Wang, Ling Feng, Yiheng Ma, Wanjing Abdel-Aty, Mohamed Tongji Univ Coll Transportat Engn Shanghai 201804 Peoples R China Purdue Univ Lyles Sch Civil & Construct Engn W Lafayette IN 47907 USA Univ Cent Florida Dept Civil Environm & Construction Engn Orlando FL 32816 USA

Previous studies in predicting crash risks primarily associated the number or likelihood of crashes on a road segment with traffic parameters or geometric characteristics, usually neglecting the impact of vehicles' continuous movement and interactions with nearby vehicles. Recent technology advances, such as Connected and Automated Vehicles (CAVs) and drones, are able to collect high-resolution trajectory data, which enable trajectory-based risk analysis. This study investigates a new vehicle group (VG) based risk analysis method and explores risk evolution mechanisms considering VG features. An impact-based vehicle grouping method is proposed to cluster vehicles into VGs by evaluating their responses to the erratic behaviors of nearby vehicles. The risk of a VG is aggregated based on the risk between each vehicle pair in the VG, measured by inverse Time-to-Collision (iTTC). Logistic regression and a Graph Neural Network (GNN) are used to predict VG risks based on both aggregated and disaggregated VG information. Both methods achieve excellent performance with AUC values exceeding 0.93. For the GNN model, GNNExplainer with feature perturbation is applied to identify critical individual vehicle features and their directional impact on VG risks. Overall, this research contributes a new perspective for identifying, predicting, and interpreting traffic risks.

关键词： Accidents Graph neural networks Predictive models Adaptation models Trajectory Vehicle dynamics Recurrent neural networks Logistic regression Roads regression tree analysis Vehicle-group-based safety analysis vehicle trajectory data graph neural network explainable artificial intelligence

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Machine Learning-Based Life Expectancy Prediction in Developed and Developing Regions

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IEEE ACCESS 2025年 13卷 69520-69531页

作者： Ahamad, Shaik Shakeel Kumar, K. Praveen Ganesh, Sankuratri Sri Alharbi, Fayez Alharby, Sultan Awad Dendukuri, Venkata S. P. Koneru, Suvarna Vani Majmaah Univ Coll Comp & Informat Sci Dept Informat Technol Al Majmaah 11952 Saudi Arabia Deemed be Univ Siddhartha Acad Higher Educ Velagapudi Ramakrishna Siddhartha Sch Engn Dept Comp Sci & Engn Vijayawada 520007 Andhra Pradesh India Majmaah Univ Coll Comp & Informat Sci Dept Comp Sci Al Majmaah 11952 Saudi Arabia

The life expectancy of a population is a vital measure of its overall health and healthcare quality. This study use machine learning methods, notably XGBoost, to predict life expectancy in industrialized and emerging countries. XGBoost, or Extreme Gradient Boosting, is a robust machine-learning technique renowned for its precision and efficiency in handling structured data. The objective is to develop a precise prediction model and identify the primary factors influencing life expectancy. The experimental findings indicate that our XGBoost-based model effectively forecasts life expectancy in both industrialized and developing nations. Mean Absolute Error, Mean Squared Error, and R-2 score are used for model selection. XGBoost is selected because to its mean squared error of 2.45, mean absolute error of 1.03, and R-2 scores of 0.99 for the training set and 0.97 for the testing set. In this a feature significance analysis was conducted to identify the most relevant factors affecting life expectancy. Our results underscore the significance of variables such as HIV/AIDS, income composition, and adult mortality in influencing life expectancy. The model's findings may assist lawmakers and healthcare professionals in making decisions to enhance public health outcomes, allocate resources efficiently, and formulate focused solutions.

关键词： Predictive models Feature extraction Machine learning Correlation Accuracy Training Prediction algorithms Data models regression tree analysis Mortality Feature importance life expectancy machine learning XGBoost

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Anomaly Detection Workflow Using Random Forest Regressor in Large-Scale Photovoltaic Power Plants

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IEEE ACCESS 2025年 13卷 54168-54176页

作者： Silva, Joao Lucas de Souza de Paula, Marcelo Vinicius Barros, Juliana de Souza Granja Barros, Tarcio Andre dos Santos Univ Estadual Campinas Power Elect Labs BR-13083870 Campinas Brazil

In large-scale photovoltaic (PV) plants, anomalies can affect performance, and maintenance schedules, and compromise long-term reliability, reducing the financial returns from the PV plant. Although the importance of anomaly detection in this context is clear, it poses a significant challenge due to the vast amount of data generated by the plant. Therefore, tools that assist in the anomaly detection process, minimizing human intervention, are essential. This paper proposes a workflow using a random forest regressor, with the application of a dynamically modeled mathematical threshold for anomaly detection. The model predicts the target power using features such as plane-of-array (POA) irradiance and temperature. To this end, Random Forest was selected after testing with other presented models, followed by the implementation of a mathematical model to establish a dynamic threshold that classifies the data as normal, alert, or anomaly. The threshold is calculated by comparing the mean absolute difference between predicted and actual power and adjusting it based on a dynamic multiplier to establish alert and anomaly limits. Subsequent tests were conducted on different inverters, applying various dataset splits. The workflow achieved excellent accuracy in detecting anomalies and alerts, with a total accuracy of 99.69%. The model successfully predicted anomalies in different inverters within the same plant. Thus, it is expected that the application of prediction methods, combined with the dynamic threshold, will serve as a support tool for anomaly detection in large-scale PV power plants.

关键词： Mathematical models Anomaly detection Random forests Training Inverters Predictive models Testing Temperature distribution Sensors regression tree analysis large-scale PV power plant photovoltaic workflow

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Design and HDL Implementation of Pulse-Arrival-Time Estimation Using XGBoost regression With tree-Recycling Architecture

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IEEE ACCESS 2025年 13卷 27596-27609页

作者： Choi, Hyo-Jeong Um, Ji-Yong Kumoh Natl Inst Technol Dept Med IT Convergence Engn Gumi Si 39253 South Korea

This paper presents a pulse-arrival-time (PAT) estimation scheme using Extreme Gradient Boosting (XGBoost) regression and its implementation with hardware description language (HDL). PAT is a significant metric for the estimation of pulse wave velocity (PWV) and blood pressure. We leverage morphological features of single photoplethysmogram (PPG) waveforms and subject-specific parameters to estimate PAT values through a decision tree ensemble regression model. XGBoost regression is adopted for PAT estimation through a comparative study conducted in software. To enable the integration of the regression model into a single chip, we propose a tree-recycling architecture for an area-efficient implementation of the decision tree ensemble model. The proposed PAT estimation has been implemented on a field-programmable gate array (FPGA, Artix7, Xilinx) for real-time verification using a dataset. Additionally, the active area of the proposed scheme in an integrated circuit was estimated after the place-and-route (PnR) procedure, resulting in an area of 1.44 mm2. We utilized the Vital Signs DataBase (VitalDB) for training and testing the proposed PAT estimation in a software-hardware co-design framework. Validation on 80 subjects yielded a determination coefficient (R2) of 0.983 in the linear regression analysis after implementing the HDL on an FPGA.

关键词： Estimation Hardware Biomedical monitoring regression tree analysis Field programmable gate arrays Elasticity Hardware design languages Computer architecture Boosting Training Pulse arrival time decision tree ensemble model extreme gradient boosting regression field programmable gate array tree-recycling architecture hardware description language

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Multi-Kernel Learning for Heterogeneous Data

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IEEE ACCESS 2025年 13卷 45340-45349页

作者： Liao, Chunlan Peng, Shili Guangzhou Panyu Polytech Guangzhou 511483 Peoples R China Guangdong Univ Finance Dept Internet Finance Guangzhou 510521 Peoples R China

Multi-kernel learning is an excellent machine learning algorithm widely used in various learning tasks such as classification and regression. Traditional kernel methods mainly focus on numerical data and lack sufficient research on categorical and mixed data. However, mixed data is widely used in practical applications, and many unstructured data can be converted into mixed data through appropriate preprocessing. In this work, we propose a new Heterogeneous Multi-Kernel Learning (HMKL) algorithm for processing mixed data containing both categorical and numerical attributes. In HMKL, category attributes and numerical attributes are processed separately. Different similarity measurement methods are used to obtain different kernel matrices for category attributes, which are then fused with numerical kernel matrices to improve the classification performance of multi-kernel learning. We propose a new ratio Gaussian kernel function for category attributes, which can maintain a balance between the AND and OR operations of the matching kernel matrix. In addition, to address the curse of dimensionality caused by one-of-N encoding, we use the summation of matching kernel matrices to reduce the difficulty of preprocessing categorical attributes. The experiment shows that our proposed HMKL algorithm can effectively handle mixed data and has excellent performance.

关键词： Kernel Encoding Distance measurement Support vector machines Training Symbols regression tree analysis Hamming distances Data processing Transforms Multi-kernel learning support vector machine categorical data mixed data kernel methods

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Enhancing Sports Team Management Through Machine Learning

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IEEE ACCESS 2025年 13卷 55431-55441页

作者： Zhang, Ling An, Yifan Inner Mongolia Agr Univ Vocat & Tech Coll Hohhot 010010 Inner Mongolia Peoples R China

Sports like basketball and baseball have seen significant improvements through the effective use of sports analytics. However, research on machine learning in football has primarily focused on prediction. There is a need to explore whether machine learning can contribute to generating more in-depth and accurate insights into football analysis. This essay presents a descriptive analysis of player performance in football matches. Typically, player performance in football is evaluated by consulting a panel of experts, though the criteria they use for evaluation remain undisclosed. By applying machine learning algorithms and pruning techniques, several classifications were developed to identify the most critical functional attributes of players. The functional metrics that delivered the best results in rating predictions had an average error of 0.17. The most significant performance data for each position on the field were closely aligned with the factors experts consider when grading players. The 34 performance characteristics identified achieved a prediction accuracy of 63.4% for match outcomes.

关键词： Sports Machine learning Linear regression Games Accuracy regression tree analysis Data models Analytical models Standards Training sports teams sports analysis

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Hardware-Optimized regression tree-Based Sigmoid and Tanh Functions for Machine Learning Applications

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第1期72卷 283-287页

作者： Roshan, Akash Dev Guha, Prithwijit Trivedi, Gaurav Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 India

The sigmoid and tangent (tanh) functions are widely recognized as the most commonly employed nonlinear activation functions in artificial neural networks. These functions incorporate exponential terms to introduce nonlinearity, which imposes significant challenges when realized on hardware. This brief presents a novel approach for the hardware implementation of sigmoid and tanh functions, leveraging a regression tree and linear regression. The proposed method divides their nonlinear region into small segments using a regression tree. These segments are further approximated using a linear regression technique, the line of best fit. Experimental results demonstrate the average errors of 4 x 10(-4) and 9x 10(-4) of sigmoid and tanh functions compared to exact functions. The above functions produce 24.52% and 35.71% less average error than the best contemporary method when implemented on the hardware. Additionally, the hardware implementations of sigmoid and tanh functions are more area, power and delay efficient, showcasing the effectiveness of this method compared to other state-of-the-art designs.

关键词： Hardware regression tree analysis Approximation methods Accuracy Table lookup Resource management Delays Multiplexing Registers Performance evaluation Sigmoid tanh regression tree linear regression neural networks VLSI architecture

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