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Advanced autism detection and visualization through xgboost algorithm for fNIRS hemo-dynamic signals

EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Farooq, Sajid He, Hailun Guo, Deyu Feng, Yuyi Hang, Jingguo Kong, Dexing He, Sailing Zhejiang Normal Univ Coll Math Med Jinhua 321004 Peoples R China Zhejiang Univ Taizhou Hosp Taizhou Peoples R China Zhejiang Univ Natl Engn Res Ctr Opt Instruments Ctr Opt & Electromagnet Res Hangzhou Peoples R China Wenzhou Med Univ Taizhou Hosp Zhejiang Prov Taizhou 318050 Peoples R China Royal Inst Technol Sch Elect Engn Dept Electromagnet Engn S-10044 Stockholm Sweden

Early detection of Autism Spectrum Disorder (ASD), a neuro-developmental condition characterized by impairments in social communication, is critical for prompt intervention and care. Recent advancements in digital medicine have significantly enhanced the precision and efficiency of ASD diagnosis, management, and care coordination. These technologies offer considerable potential for optimizing treatment pathways and improving patient outcomes. However, challenges remain in sustaining long-term user engagement and effectively integrating these innovations into routine clinical practice for ASD management. In this study, we report the findings from a clinic-based, prospective investigation aimed at evaluating the diagnostic validity of ASD employing xgboost algorithm. Of the 51 cases assessed, 24 were diagnosed with ASD, while 27 were identified as having developmental delay without autism. Functional near-infrared spectroscopy (fNIRS) was employed to measure resting-state hemo-dynamic fluctuations in the bilateral temporal lobes, revealing connectivity differences through computer vision and machine learning analysis. Agglomerative hierarchical clustering (AgHC) was employed to evaluate the active coupling between fluctuations of deoxygenated hemoglobin (HbR) and oxygenated hemoglobin (HbO) across specific channels, elucidating inter-channel relationships in neurovascular coupling. The algorithm, using a range of significant features, achieved robust diagnostic performance with area under receiver operating characteristic curve of 0.99 +/- 1, demonstrating sensitivity of 98%, specificity of 95%, negative predictive value (NPV) of 98%, and positive predictive value (PPV) of 95%, indicating high diagnostic accuracy in distinguishing relevant cases. These findings underscore the potential of digital medicine to offer objective and scalable framework for the diagnosis of autism in real-world clinical environments.

关键词： Autism xgboost algorithm Machine learning fNIRS UMAP

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Modeling and Estimation Methods for Student Achievement Recognition Based on xgboost algorithm

Modeling and Estimation Methods for Student Achievement Reco...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Li, Guangpeng Zhou, Hengchao Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9798350313413

With the development of deep learning related algorithms, the target requirements of performance recognition models are constantly being optimized. The research on student performance recognition modeling and estimation methods based on xgboost (Extreme Gradient Boosting) algorithm is becoming increasingly important. In the construction of the entire student achievement recognition and estimation model, how to improve the accuracy of the improved model's achievement estimation and the efficiency of the system recognition is currently a key issue that needs to be urgently addressed. This article conducts research on traditional student performance prediction methods, utilizes the application of xgboost algorithm in student performance, analyzes the algorithm process steps, and combines the performance evaluation system of student performance classification. Based on the data results, the following conclusions are drawn through discussion. Six subject samples were selected through simulation experiments, and the student achievement recognition and estimation model based on xgboost algorithm was applied to improve the accuracy of achievement estimation compared to traditional schemes, with a comprehensive average improvement of about 8.4%. At the same time, there have been improvements in the efficiency of system identification, and the overall average improvement is about 17.8%. This indicates that the student achievement recognition and estimation model based on xgboost algorithm has good practical application effect in predicting student achievement. Finally, this study also provides data-based decision-making support for education policy makers, helping to optimize the allocation of educational resources, improve education quality, promote academic success and personal development of students, and thus contribute important methods and tools to the improvement and enhancement of the education system. © 2023 IEEE.

关键词： grade prediction identification estimation student grades xgboost algorithm

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Fusion State-of-Health Estimation of Lithium-Ion Batteries Based on Improved xgboost algorithm and Adaptive Kalman Filter

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2024年第12期171卷 120506-120506页

作者： Tan, Piqiang Zhao, Luhao Yang, Xiaomei Yang, Aoji Liu, Xiaoyang Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

Accurately predicting the state-of-health of lithium-ion batteries (LIBs) is of paramount significance for safety and stability of battery systems. This paper introduces a fusion model, which integrates the characteristic of data-driven model and equivalent circuit model to enhance precision. The first step is to preprocess data, including extracting health features, correlation screening, and compressing data. Subsequently, the hyperparameters of xgboost algorithm are optimized using a weighted artificial bee colony algorithm, resulting in an improved xgboost (IXGB) data-driven model. Finally, the observed values from the data-driven model and the prior values based on the equivalent circuit model are combined through adaptive Kalman filter (AKF), developing an improved xgboost and adaptive Kalman filter (IXGB-AKF) fusion model, which makes the most of historical experience and the current state of LIBs. Validation is conducted using publicly available NASA Li-ion Battery Aging Datasets, with different datasets under various operating conditions, including different battery cells, different discharge depths and rates of LIBs. The resulting root mean square error values of the former three operating conditions are 1.834%, 2.570%, and 3.456%, respectively. The results indicate that the IXGB-AKF fusion model exhibits good accuracy and robustness under different operating conditions. A novel SOH fusion model is established with XGBoot and AKFSix health features are finally selected and compressed for SOH estimationBattery cells, discharge depths and rates are conducted to verify modelsWeighted artificial bee colony algorithm is used to optimize hyperparameters

关键词： batteries - li-ion energy storage state-of-health fusion model adaptive kalman filter xgboost algorithm

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Risk Assessment Approach of Electronic Component Selection in Equipment R&D Using the xgboost algorithm and Domain Knowledge

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PROCESSES 2024年第8期12卷 1716页

作者： Wu, Chuanwen Zhang, Shumei Bao, Xiaoli Wang, Yang Miao, Zhikun Liu, Huixin Aerosp Sci & Ind Def Technol Res & Test Ctr Beijing 100854 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Risk management in electronic component selection is crucial for ensuring inherent system quality dependability in aerospace equipment research and development (R&D). Therefore, it is of great significance to conduct rapid and accurate risk assessment research of electronic components based on engineering practice. This article utilizes the extreme gradient boosting (xgboost) algorithm and domain knowledge to assess electronic component selection risk. Firstly, an innovative risk assessment system is established for electronic component selection based on business materials analysis and investigation by questionnaire. Then, the values of factors in the system are quantified based on domain knowledge and empirical formulae. Finally, an xgboost-based risk assessment model is constructed that can explore learning strategies and develop latent features by integrating multiple decision trees. The model is compared against the random forest (RF), support vector machine (SVM) and decision tree (DT) algorithms. Accuracy, precision, recall, and F1 score are used as evaluation indexes. The results obtained from the above algorithms illustrate the effectiveness of the proposed method in electronic component selection risk assessment.

关键词： electronic components risk assessment factor analysis xgboost algorithm

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Prediction of Oil-Water Two-Phase Flow Patterns Based on Bayesian Optimisation of the xgboost algorithm

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PROCESSES 2024年第8期12卷 1660页

作者： Wang, Dudu Guo, Haimin Sun, Yongtuo Liang, Haoxun Li, Ao Guo, Yuqing Yangtze Univ Coll Geophys & Petr Resources Wuhan 430100 Peoples R China Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China

With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil-water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (xgboost, version 2.1.0) optimised through Bayesian techniques (using the Bayesian-optimization library, version 1.4.3) to predict oil-water two-phase flow regimes. The integration of Bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional xgboost model and its Bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the Bayesian-optimised xgboost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional xgboost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the Bayesian-optimised xgboost (BO-xgboost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional xgboost model. Comparative analyses demonstrate that Bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclinatio

关键词： horizontal well vertical well inclined well Bayesian algorithm xgboost algorithm flow pattern prediction

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A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and xgboost algorithm

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IEEE ACCESS 2021年 9卷 154172-154182页

作者： Zhang, Neng Qian, Huimin He, Yuchao Li, Lirong Sun, Chaoyun Wuhan Nari Ltd Liabil Co State Grid Power Res Inst Wuhan 430000 Peoples R China

Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (xgboost) algorithm is proposed. In this method, the transient stability of all typical fault scenarios of power system is obtained firstly by xgboost. Then, to make full use of the structure of the raw data and mine the contained data information, a novel data mining algorithm KODAMA is introduced to cluster the mode of rotor angle in case of instability, thus to convert pattern-unlabeled case data into pattern-labeled data. Finally, based on this labeled data, to fully reflect the dynamic characteristics, a multiple xgboost assessment strategy is designed to recognize different instable modes. The proposed technique is tested on the Nordic test system, and the results indicate that the proposed approach can provide fast and accurate recognition of instable mode and has a certain prospect of online application.

关键词： Power system stability Transient analysis Rotors Generators Stability criteria Clustering algorithms Data models Transient stability KODAMA algorithm xgboost algorithm machine learning

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xgboost algorithm-based prediction of concrete electrical resistivity for structural health monitoring

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AUTOMATION IN CONSTRUCTION 2020年 114卷 103155-000页

作者： Dong, Wei Huang, Yimiao Lehane, Barry Ma, Guowei Univ Western Australia Dept Civil Environm & Min Engn Perth WA 6009 Australia Hebei Univ Technol Dept Civil & Transportat Engn Tianjin 300401 Peoples R China

For structural health monitoring, electrical resistivity measurement (ERM) method is commonly employed for the detection of concrete's durability, as indicated by the chloride permeability and the corrosion of steel reinforcement. However, according to previous experimental studies, ERM results are susceptible to significant uncertainties due to multiple influencing factors such as concrete water/cement ratio and structure curing environment as well as their complex interrelationships. The present study therefore proposes an xgboost algorithm-based prediction model which considers all potential influential factors simultaneously. A database containing 800 experimental instances composed of 16 input attributes is constructed according to existing reported studies and utilized for training and testing the xgboost model. Statistical scores (RMSE, MAE and R-2) and the GridsearchCV feature are applied to evaluate and optimize the established model respectively. Results show that the proposed xgboost model achieves satisfactory predictive performance as demonstrated by high coefficients of regression fitting lines (0.991 and 0.943) and comparatively low RMSE values (4.6 and 11.3 k5 Omega cm) for both training and testing sets respectively. The analyses of the attribute importance ranking also reveal that curing age and cement content have the greatest influence on ERM results.

关键词： Concrete electrical resistivity Structural health monitoring Machine learning xgboost algorithm

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Modal Parameter Identification of Civil Engineering Structure Based on xgboost algorithm

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Procedia Computer Science 2023年 228卷 446-454页

作者： Xuguang Zhang Zhiying Tian College of Architecture and Civil Engineering Chongqing Metropolitan College of Science and Technology Chongqing China

In order to prevent the structural problems of buildings and related civil engineering from becoming potential risks, the modal parameter identification method of civil engineering structures is studied based on xgboost algorithm. First of all, it is necessary to design and extract the modal parameters of civil engineering structures, and then detect civil engineering structures according to the modal parameter identification model, identify the existing problems of the structures, discuss and analyze them, verify the effect of the detection method of civil engineering structures based on modal parameter identification, and make a comparative analysis with the traditional modal parameter identification methods such as SSI. By analyzing the basic principle and algorithm of xgboost algorithm, this study analyzes the research characteristics and present situation of the method, identifies the implementation and feasibility of modal parameters of civil engineering structures, and provides theoretical basis and data reference for the development of related professions and related industries.

关键词： xgboost algorithm Civil Engineering Structural Mode Parameter Identification Modal of Civil Engineering Structure

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Clinical Data based xgboost algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study

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BMC GASTROENTEROLOGY 2023年第1期23卷 310-310页

作者： Zheng, Jing Li, Jianjun Zhang, Zhengyu Yu, Yue Tan, Juntao Liu, Yunyu Gong, Jun Wang, Tingting Wu, Xiaoxin Guo, Zihao Chongqing Med Univ Banan Hosp Operat Management Off Chongqing 401320 Peoples R China Chongqing Med Univ Banan Hosp Dept Cardiothorac Surg Chongqing 401320 Peoples R China Zhejiang Univ Affiliated Hosp 1 Sch Med Med Records Dept Hangzhou 310003 Peoples R China Mayo Clin Sr Bioinformatician Dept Quantitat Hlth Sci Rochester MN 55905 USA Chongqing Med Univ Affiliated Hosp 2 Med Records Dept Chongqing 400010 Peoples R China Chongqing Med Univ Univ Town Hosp Dept Informat Ctr Chongqing 401331 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Natl Clin Res Ctr Infect DisState Key Lab Diag & 79 Qing Chun Rd Hangzhou 310003 Zhejiang Peoples R China Chongqing Banan Canc Hosp Dept Gastroenterol Chongqing 400054 Peoples R China

Objectives To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using xgboost algorithm in a retrospective case-control study. Methods Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (xgboost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree xgboost model. Finally, the simple-tree xgboost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree xgboost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree xgboost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P<0.001). Conclusions The simple-tree xgboost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.

关键词： Decompensated cirrhosis Infection xgboost algorithm Prediction model Multicenter

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Application of xgboost algorithm in the optimization of pollutant concentration

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ATMOSPHERIC RESEARCH 2022年 276卷

作者： Li, Jiangtao An, Xingqin Li, Qingyong Wang, Chao Yu, Haomin Zhou, Xinyuan Geng, Yangli-ao Chinese Acad Meteorol Sci State Key Lab Severe Weather & Key Lab Atmospher C Beijing 100081 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

With the rapid development of the economy, urban pollution has become a hot issue of human concern, and people are put forward higher requirements for the accuracy of pollutant concentration simulations. Based on the the WRF-Chem model simulation results and Beijing environmental monitoring data, this study constructed the xgboost algorithm through the process of data cleaning, feature selection and super parameter optimization, and the optimized simulation of PM2.5 and O3 concentrations in Beijing was then carried out based on the constructed algorithm. The results showed that the xgboost algorithm can better capture the spatial and temporal variation patterns of pollutant concentrations, and has a greater improvement on the simulation results of the WRF-Chem model, and that the xgboost algorithm shows better optimisation results in urban areas compared to suburban areas. In addition, the overall analysis of the features of PM2.5 and O3 concentrations based on the SHAP value theory showed that the time series and periodic features, aerosol ion concentration of Sodium (NAAJ) and Nitrate concentration (NO3AJ) were the important features affecting the prediction of PM2.5 concentration by the xgboost algorithm, and the most important factor affecting O3 concentration is temperature. PM2.5 and O3 concentrations were divided into three levels, and several samples were selected for single sample analysis. The analysis showed that at low pollutant concentration, most of the features made negative contributions to the concentration prediction, while at high concentration, most of the features made positive contributions. The contribution values of different features varied greatly and were unevenly distributed. The results of prediction were basically composed of a few features with large feature contributions, and the feature contributions of the same feature to different concentration prediction results were also different. Moreover, the xgboost algorithm was used to opti

关键词： xgboost algorithm WRF-Chem model SHAP value Feature contributions O3 PM2 5

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