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tree-based algorithms and Association Rule Mining for Predicting Patients' Neurological Outcomes After First-Aid Treatment for an Out-of-Hospital Cardiac Arrest During COVID-19 Pandemic Application of Data Mining

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INTERNATIONAL JOURNAL OF GENERAL MEDICINE 2022年 15卷 7395-7405页

作者： Lin, Wei-Chun Huang, Chien-Hsiung Chien, Liang-Tien Tseng, Hsiao-Jung Ng, Chip-Jin Hsu, Kuang-Hung Lin, Chi-Chun Chien, Cheng-Yu New Taipei Municipal TuCheng Hosp Dept Emergency Med New Taipei Taiwan Chang Gung Univ New Taipei Taiwan Chang Gung Univ Dept Emergency Med Chang Gung Mem Hosp Linkou Taiwan Chang Gung Univ Coll Med Taoyuan Taiwan Chang Gung Mem Hosp Dept Emergency Med 5 Fushing St Taoyuan Taiwan Chang Gung Univ Grad Inst Management Taoyuan Taiwan Taoyuan City Govt Fire Dept Taoyuan Taiwan Fu Jen Catholic Univ Dept Stat & Informat Sci New Taipei Taiwan Chang Gung Mem Hosp Clin Trial Ctr Biostat Unit Taoyuan Taiwan Chang Gung Mem Hosp Dept Emergency Med Taipei Taiwan Chang Gung Univ Lab Epidemiol Taoyuan Taiwan Chang Gung Univ Hlth Aging Res Ctr Dept Hlth Care Management Lab Epidemiol Taoyuan Taiwan Chang Gung Univ Sci & Technol Coll Human Ecol Res Ctr Food & Cosmet Safety Taoyuan Taiwan Ming Chi Univ Technol Dept Safety Hlth & Environm Engn New Taipei Taiwan Ton Yen Gen Hosp Dept Emergency Med Zhubei Taiwan Natl Taiwan Univ Coll Publ Hlth Inst Epidemiol & Prevent Med Taipei Taiwan

Objective: The authors performed several tree-based algorithms and an association rules mining as data mining tools to find useful determinants for neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients as well as to assess the effect of the first-aid and basic characteristics in the EMS system. Patients and Methods: This was a retrospective cohort study. The outcome was Cerebral Performance Categories grading on OHCA patients at hospital discharge. Decision tree-based models inclusive of C4.5 algorithm, classification and regression tree and random forest were built to determine an OHCA patient's prognosis. Association rules mining was another data mining method which we used to find the combination of prognostic factors linked to the outcome. Results: The total of 3520 patients were included in the final analysis. The mean age was 67.53 (+/- 18.4) year-old and 63.4% were men. To overcome the imbalance outcome issue in machine learning, the random forest has a better predictive ability for OHCA patients in overall accuracy (91.19%), weighted precision (88.76%), weighted recall (91.20%) and F1 score (0.9) by oversampling adjustment. Under association rules mining, patients who had any witness on the spot when encountering OHCA or who had ever ROSC during first-aid would be highly correlated with good CPC prognosis. Conclusion: The random forest has a better predictive ability for OHCA patients. This paper provides a role model applying several machine learning algorithms to the first-aid clinical assessment that will be promising combining with Artificial Intelligence for applying to emergency medical services.

关键词： cardiac arrest tree-based algorithms data mining

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Coastal vulnerability assessment using the machine learning tree-based algorithms modeling in the north coast of Java, Indonesia

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EARTH SCIENCE INFORMATICS 2023年第4期16卷 3981-4008页

作者： Yulianto, Fajar Wibowo, Mardi Yananto, Ardila Perdana, Dhedy Husada Fadjar Wiguna, Edwin Adi Prabowo, Yudhi Rahili, Nurkhalis Nurwijayanti, Amalia Iswari, Marindah Yulia Ratnasari, Esti Rusdiutomo, Amien Nugroho, Sapto Purwoko, Andan Sigit Aziz, Hilmi Fachrudin, Imam Natl Res & Innovat Agcy BRIN Res Ctr Hydrodynam Technol Jl Hidro Dinamika Surabaya 60112 East Java Indonesia

The north coast of Java is the center of economic activity in Indonesia. This area is dynamic and sensitive to various geo-bio-physical aspects. Therefore, a vulnerability study in this area is necessary. This study proposes a machine learning tree-based algorithms modeling approach for Coastal Vulnerability Assessment (CVA) and mapping. The tree-based algorithms used are Gradient tree Boost (GTB), Classification and Regression trees (CART), and Random Forest (RF). The study utilized the Google Earth Engine (GEE) platform and twelve variables as input. The prediction results of each of these modeling algorithms have been compared and evaluated to determine the most optimal performance and accuracy. Reference data was obtained from the Ministry of Maritime Affairs and Fisheries of the Republic of Indonesia (KKP). Approximately 70% of the reference data was allocated for training, while the remaining 30% was designated for validation. The CVA assessment yielded overall accuracies of 80.22%, 77.40%, and 71.18% based on the RF, GTB, and CART algorithms, respectively. Meanwhile, the Kappa Index for these three algorithms was 0.72, 0.67, and 0.58, indicating that the models have adequately classified the data. The research outcomes are anticipated to offer insights into the potential utilization of machine learning technology for vulnerability assessment and mapping, contributing to the management of coastal environmental issues.

关键词： Geospatial data Machine learning tree-based algorithms Vulnerability North coast of Java Indonesia

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Enhancing cell delay accuracy in post-placed netlists using ensemble tree-based algorithms

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INTEGRATION-THE VLSI JOURNAL 2024年 97卷

作者： Attaoui, Yassine Chentouf, Mohamed Ismaili, Zine El Abidine Alaoui El Mourabit, Aimad Siemens EDA CSD Calypto Synth Solut Rabat Morocco Mohammed V Univ Informat Commun & Embedded Syst ICES Team Rabat Morocco Abdelmalek Essaadi Univ Syst & Data Engn Team ENSA Tangier Tangier Morocco

Nowadays, the ASIC design is increasing in complexity, and PPA targets are pushed to the limit. The lack of physical information at the early design stages hinders precise timing predictions and may lead to design re -spins. In previous work, we successfully improved timing prediction at the post -placement stage using the Random Forest model, achieving 91.25% cell delay accuracy. Building upon this, we further investigate the potential of Ensemble tree-based algorithms, specifically focusing on " Extremely Randomized trees "and " Gradient Boosting ", to close the gap in cell delay accuracy. In this paper, we enrich the training dataset with new 16 nm industrial designs. The results demonstrate a substantial improvement, with an average cell delay accuracy of 92.01% and 84.26% on unseen data. The average Root-Mean-Square-Error is significantly reduced from 12.11 to 3.23 and 7.76 on unseen data.

关键词： Delay estimation Machine learning tree-based algorithms Post-placement delay estimation Cell delay

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Bearing capacity prediction of open caissons in two-layered clays using five tree-based machine learning algorithms

Intelligent Geoengineering

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Intelligent Geoengineering 2025年第2期2卷 55-65页

作者： Rungroad Suppakul Kongtawan Sangjinda Wittaya Jitchaijaroen Natakorn Phuksuksakul Suraparb Keawsawasvong Peem Nuaklong Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures Department of Civil Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani 12120 Thailand

Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions. However, accurately predicting their undrained bearing capacity in layered soils remains a complex challenge. This study presents a novel application of five ensemble machine (ML) algorithms—random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and categorical boosting (CatBoost)—to predict the undrained bearing capacity factor ( N c ) of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis (FELA). The input dataset consists of 1188 numerical simulations using the Tresca failure criterion, varying in geometrical and soil parameters. The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies. The ML models were trained on 70 % of the dataset and tested on the remaining 30 %. Their performance was evaluated using six statistical metrics: coefficient of determination ( R² ), mean absolute error ( MAE ), root mean squared error ( RMSE ), index of scatter ( IOS ), RMSE-to-standard deviation ratio ( RSR ), and variance explained factor ( VAF ). The results indicate that all the models achieved high accuracy, with R² values exceeding 97.6 % and RMSE values below 0.02. Among them, AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets, demonstrating superior generalizability and robustness. The proposed ML framework offers an efficient, accurate, and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils. This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.

关键词： Two-layered clay Open caisson tree-based algorithms FELA Machine learning

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Machine learning-based prediction of hydrogen-assisted fatigue crack growth rate in Cr-Mo steel

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 122卷 1-11页

作者： Hu, Jiangchuan Ma, Kai Zhang, Zhenquan Zhang, Ruiming Zheng, Jinyang Zhejiang Univ Inst Adv Equipment Hangzhou 310027 Zhejiang Peoples R China MOE Engn Res Ctr High Pressure Proc Equipment & Safety Beijing Peoples R China Zhejiang Univ Key Lab Hydrogen Storage & Transportat Technol Zhe Hangzhou 310027 Zhejiang Peoples R China

In this study, linear and tree-based algorithms were utilized to train the hydrogen-assisted fatigue crack growth test data. Through accuracy verification, it was found that the Gradient Boosting Regression (GB) belonging to tree-based algorithms had the best predictive performance. For the model trained by the GB algorithm, SHapley Additive exPlanations (SHAP) values were employed to evaluate the impact of stress intensity factor range, hydrogen pressure, ultimate tensile strength, stress ratio, frequency and chemical compositions on the hydrogenassisted fatigue crack growth. The results show that material properties, experimental conditions and environment all have an effect on the crack growth rate. Besides, the feature influence patterns derived from machine learning models are consistent with the literature, demonstrating that the model has the potential to accurately predict the hydrogen-assisted fatigue crack growth rate of Cr-Mo steel.

关键词： Machine learning Linear algorithms tree-based algorithms Cr-Mo steel Hydrogen-assisted fatigue crack growth rate Prediction model

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Optimized decision tree algorithms to estimate ultimate strain of concrete wrapped by aramid fiber-reinforced polymer

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第4期8卷 1-18页

作者： Guo, Yangyang Shijiazhuang Coll Appl Technol Dept Architecture Engn Shijiazhuang 050081 Hebei Peoples R China

A number of machine learning (ML) algorithms using tree-based methodologies were developed to forecast the ultimate strain (epsilon cu) of circular columns wrapped in aramid fiber-reinforced polymer (AFRP). This effort aimed to develop and evaluate ML techniques for the assessment of epsilon cu via the use of a dataset including 156 experimental results from 19 published research papers. This purpose led to the development of the decision tree (DT). The hyperparameters of DT, as established by the Beluga whale optimization algorithm (BWOA) and the Golden jackal optimization algorithm (GJOA) (DT(B) and DT(G)), greatly influence DT's effectiveness. The practical novelty of this research lies in its development of more accurate and reliable predictive models for the epsilon cu\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varepsilon }_{cu}$$\end{document} of circular columns wrapped in AFRP by leveraging advanced optimization algorithms. These innovations enhance the precision of structural design and safety assessments, providing engineers with a robust tool for optimizing material selection, reducing costs, and improving the safety and performance of AFRP-wrapped structures across various conditions. According to forecast reliability and measurement variability, U95 evaluates uncertainty accurately. This shows a measure of dependability for informed decision-making. In learning and assessment, DT(G) had the lowest U95 index values-0.2893 and 0.2261. These values were below DT(B)'s 0.323 and 0.2476 throughout training and assessment. based on the variance percentage that was used, the variation percentage among the two models for these metrics-which is at least 8% and sometimes 42%-demonstrates the predictive power and dependability of the DT(G).

关键词： Aramid fiber Concrete Polymers Confinement Ultimate strain Machine learning tree-based algorithms Optimal structures

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Mapping climate change induced forest fire susceptibility using tree-based machine learning algorithms in Bangladesh

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Ecological Indicators 2025年 176卷

作者： Mafrid Haydar Al Hossain Rafi Md.Kamran Hasan Khan Sakib Hosan Halima Sadia Department of Urban and Regional Planning Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh

Forest fires in forested areas are an environmental disaster resulting from either natural phenomena or human activities. Forest fires pose a substantial risk in the forested and grassland areas of the Chittagong Hill Tracts. Therefore, mitigating the danger of forest fires is crucial for lessening their potential negative effects. This study employs tree-based machine learning (ML) algorithms to assess Forest Fire Susceptibility in the Chittagong Hill Tracts (CHT) of Bangladesh under current and anticipated climate change scenarios. Nineteen environmental and climatic variables were analyzed, with temperature, precipitation, wind speed, and height identified as the most relevant factors. This research analyses 267 historical instances of forest fires. Historical fire data and environmental attributes were utilized to train and validate eight ML models: Random Forest (RF), LightGBM, XGBoost, Decision tree (DT), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Bagging. AdaBoost had the highest predictive accuracy (AUC: 0.82), classifying 4.31% of the study area as very susceptible, 66.74% as moderately susceptible, and 28.94% as having low Susceptibility. Future fire Susceptibility were projected under Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) for respective years of 2040, 2060, 2080, and 2100, indicating an increase in fire-prone regions, with high-susceptible areas expected to grow by as much as 15.39% under RCP 8.5 by 2080. These findings underscore the growing vulnerability of forested regions due to climate change and highlight the necessity of implementing proactive fire management strategies. The results of this research give significant novel insights for authorities in forestry administration and legislators, facilitating the development of climate-adaptive fire mitigation policies to strengthen environmental resilience and safeguard natural resources, while also supporti

关键词： Climate change Fire risk tree-based algorithms Disaster management Sustainable forest management

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Exploration of women cyclists’ perceived security using tree-based machine learning algorithms

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Procedia Computer Science 2023年 220卷 624-631页

作者： Peyman Noorbakhsh Navid Khademi Krisada Chaiyasarn Department of Civil Engineering Faculty of Engineering Thammasat University (Rangsit Campus) Pathumthani Thailand School of Civil Engineering College of Engineering University of Tehran Tehran Iran

The inclusion of human behavioral factors in travel demand analysis is a new trend in transportation planning. Perceived security (PS) is one of the crucial behavioral factors governing urban travelers' choices and demand analysis, exclusively for active modes of transportation like bicycles and vulnerable road users like women. However, no scientific attempt has been made to explain and quantify PS for women cyclists. We estimate women cyclists’ PS by considering individual, social, and built environment features from the data collected in Tehran, Iran. Fifty-two women were recruited as the test participants in a VR-based bicycle experimental system (VRBES). For analysis, we focused on the tree-based machine learning (ML) methods besides an econometric model. In the end, the random forest (RF) algorithm is chosen and improved for the best performance (with test-R2=0.69) on the dataset, and feature importance showed population, a social feature, is the most important.

关键词： Travel behavior Perceived security Women Cyclists Machine learning tree-based algorithms Random forest algorithm

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The Predictability of tree-based Machine Learning algorithms in the Big Data Context

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第1期34卷 82-89页

作者： Qolipour, F. Ghasemzadeh, M. Mohammad-Karimi, N. Yazd Univ Dept Comp Engn Yazd Iran

This research work is concerned with the predictability of ensemble and singular tree-based machine learning algorithms during the recession and prosperity of the two companies listed in the Tehran Stock Exchange in the context of big data. In this regard, the main issue is that economic managers and the academic community require predicting models with more accuracy and reduced execution time;moreover, the prediction of the companies recession in the stock market is highly significant. Machine learning algorithms must be able to appropriately predict the stock return sign during the market downturn and boom days. Addressing the stated challenge will upgrade the quality of stock purchases and, subsequently, will increase profitability. In this article, the proposed solution relies on the utilization of tree-based machine learning algorithms in the context of big data. The proposed solution exploits the decision tree algorithm, which is a traditional and singular tree-based learning algorithm. Furthermore, two modern and ensemble tree-based learning algorithms, random forest and gradient boosted tree, has been utilized for predicting the stock return sign during recession and prosperity. The mentioned cases were implemented by applying the machine learning tools in python programming language and PYSPARK library that is used explicitly for the big data context. The utilized research data of the current study are the shares information of two companies of the Tehran Stock Exchange. The obtained results reveal that the applied ensemble learning algorithms have performed better than the singular learning algorithms. Additionally, adding 23 technical features to the initial data and subsequent applying of the PCA feature reduction method have demonstrated the best performance among other modes. In the meantime, it has been concluded that the initial data do not possess the proper resolution or generalizability, either during prosperity or recession.

关键词： Stock Market Big Data Prediction Machine Learning tree-based algorithms Ensemble algorithms

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Machine Learning Models for Predicting Bond Strength of Deformed Bars in Concrete

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ACI STRUCTURAL JOURNAL 2022年第5期119卷 43-56页

作者： V. Degtyarev, Vitaliy New Millennium Bldg Syst LLC Ft Wayne IN 46804 USA

This paper proposes eight machine learning models for predicting the bond strength between straight deformed reinforcing bars and concrete under tensile load. The models included support vector regressor (SVR), k-nearest neighbors (KNN), random forest (RF), gradient boosting regressor (GBR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), gradient boosting with categorical features support (CatBoost), and adap-tive boosting (AdaBoost). The models were trained using a large data set of test results from the Joint ACI-ASCE Committee 408 database. The performance of the models was evaluated through a robust tenfold cross-validation method. Optimal parameters for each model were established through an extensive tuning process. The bond strengths predicted by the proposed models agree well with the experimental data. Their accuracy exceeds the accuracy of the available descriptive equations. The relative feature importance for predicting the bond strength was evaluated using the permuta-tion and Shapley additive explanations (SHAP) methods for each model. Partial dependence of the bond force from the considered features was also presented and discussed.

关键词： bond deformed reinforcement ensemble learning gradient boosting k-nearest neighbor machine learning splice support vector machine tree-based algorithms

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