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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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Long tunnel group driving fatigue detection model based on xgboost algorithm

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Journal of Traffic and Transportation Engineering(English Edition) 2025年第1期12卷 167-179页

作者： Huazhi Yuan Kun Zhao Ying Yan Li Wan Zhending Tian Xinqiang Chen School of Civil Engineering Lanzhou University of TechnologyLanzhou 730050China School of Transportation Engineering Chang'an UniversityXi'an 710064China Shandong Transportation Planning and Design Institute Group Co. Ltd.Jinan 250031China China Railway First Survey and Design Institute Group Co. Ltd.Xi'an 710043China Institute of Logistics Science and Engineering Shanghai Maritime UniversityShanghai 201306China

Driving fatigue is one of the important causes of accidents in tunnel(group)*** this paper,in order to effectively identify the driving fatigue of tunnel(group)drivers,an eye tracker and other instruments were used to conduct real vehicle tests on long tunnel(group)expressways and thus obtain the eye movement,driving duration,and Karolinska sleepiness scale(KSS)data of 30 *** impacts of the tunnel and non-tunnel sections on drivers were compared,and the relationship between blink indexes,such as the blink frequency,blink duration,mean value of blink duration,driving duration,and driving fatigue,was studied.A paired t-test and a Spearman correlation test were performed to select the indexes that can effectively characterize the tunnel driving fatigue.A driving fatigue detection model was then developed based on the xgboost *** obtained results show that the blink frequency,total blink duration,and mean value of blink duration gradually increase with the deepening of driving fatigue,and the mean value of blink duration is the most sensitive in the tunnel *** addition,a significant correlation exists between the driving duration index and driving fatigue,which can provide a reference for improving the tunnel *** the mean value of blink duration and driving duration as the characteristic indexes,the accuracy of the driving fatigue detection model based on the xgboost algorithm reaches 98%.The cumulative and continuous tunnel proportion effectively estimates the driving fatigue state in a long tunnel(group)environment.

关键词： Traffic safety Tunnel group Driving fatigue detection Eye movement data Driving duration xgboost algorithm

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Machine learning-based assessment of thermal comfort for the elderly in warm environments: Combining the xgboost algorithm and human body exergy analysis

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 209卷

作者： He, Mengyuan Liu, Hong Zhou, Shan Yao, Yan Kosonen, Risto Wu, Yuxin Li, Baizhan Chongqing Univ Joint Int Res Lab Green Bldg & Built Environm Minist Educ Chongqing Peoples R China Chongqing Univ Natl Ctr Int Res Low carbon & Green Bldg Minist Sci & Technol Chongqing Peoples R China Aalto Univ Dept Mech Engn Espoo 02150 Finland Zhejiang Sci Tech Univ Sch Civil Engn & Architecture Hangzhou Peoples R China

Many elderly people rarely own or use air conditioners because of low income and economising habits, causing them to live in warm thermal environments when heat waves and hot weather occur. Living in warm conditions worsens thermal discomfort and poses health risks this group. To investigate the thermal comfort and adaptation of the elderly, a total of 38 participants were recruited for two parts of experiments in a climate chamber: Part A collected thermal sensation vote (TSV) and physiological parameters for 30 min at 28, 30, and 32 degrees C, and Part B presented a 20-min cooling with fans (air velocities of 0.6 and 1.4 m/s) at the same temperature. Furthermore, we constructed a thermal comfort model for the elderly based on human body exergy analysis and the GBDT, AdaBoost, and xgboost machine-learning algorithms. The results showed that the predicted mean vote considerably overestimated the actual TSV. The TSV and mean skin temperature were decreased by 0.1-0.5 scores and 0.4-0.5 degrees C by the behavioural adaptation of fan cooling. The predictive results showed that the xgboost model performed better, with R2 score, mean absolute error (MAE), and mean squared error (MSE) of 81 %, 0.10, and 0.01. Exergy transfer from evaporation (Ex-Esk), mean skin temperature (mtsk), air velocity (va), and convective exergy transfer (Ex-C) contributed more to the feature importance in the SHAP value analysis. The current study has implications for investigating physiological comfort and age-friendly environmental designs for the elderly, providing new perspectives for thermal comfort evaluations.

关键词： Elderly Human body exergy analysis Machine learning Thermal comfort xgboost algorithm

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Evaluation and Application of the PT-JPL Physical Model Optimized with xgboost algorithm in Latent Heat Flux Estimation

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WATER RESOURCES MANAGEMENT 2025年 1-18页

作者： Wang, Lizheng Kong, Jinling Zhang, Qiutong Dong, Lixin Zhong, Yanling Changan Univ Sch Geol Engn & Geomat Xian 710054 Peoples R China China Meteorol Adm Natl Satellite Meteorol Ctr Natl Ctr Space Weather Key Lab Radiometr Calibrat & Validat Environm Sate Beijing Peoples R China

The static parameterization scheme in the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model limits the dynamic capture of latent heat flux (LE) in different plant functional types (PFTs). Therefore, this study employs the Extreme Gradient Boosting (xgboost) algorithm to optimize the constraint factors and sub-models in the PT-JPL model that are influenced by sensitive prior parameters, thereby constructing five hybrid models under the PT-JPL physical constraint framework to achieve the dynamic response of fixed prior parameters and to integrate ensemble learning (EML) with the process-based framework, ensuring that physical mechanism and high precision coexist. Comparative analysis and validation across five PFTs in the Heihe River Basin of China reveal that the XGB-LEc-PT-JPL model, optimized for vegetation transpiration, exhibits the best comprehensive performance and outperforms the pure data-driven model in several aspects. Regarding overall accuracy, the MAE and RMSE are 15.47 W/m(2) and 23.85 W/m(2), respectively. Although hybrid models optimized for deeper constraint factors sometimes exceed the simulation accuracy of the XGB-LEc-PT-JPL model, they often exhibit reduced parameter generalization, increasing model uncertainty. Finally, the regional scale comparison of different models reveals a consistent spatial pattern, and the XGB-LEc-PT-JPL model can still achieve good simulation accuracy. This study combines EML with physical model, providing scientific insights for understanding hydrological processes under regional climate change, as well as for ecological water resource conservation and optimal water resource allocation.

关键词： Evapotranspiration Combining ensemble learning algorithm with physical model PT-JPL model Physical constraints 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

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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A multi-dimensional decision framework based on the xgboost algorithm and the constrained parametric approach

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Wang, Xinxin Zhang, Bingbing Xu, Zeshui Li, Ming Skare, Marinko Sichuan Univ Business Sch Chengdu 610064 Peoples R China Juraj Dobrila Univ Pula Fac Econ & Tourism Dr Mijo Mirkov Preradoviceva 1-1 Pula 52100 Croatia Univ Econ & Human Sci Warsaw Warsaw Poland

With the wide access to data and advanced technologies, organizations and firms prefer to use data-based and interpretable analytics to deal with uncertain and cognitive decision-making problems. In this regard, this study considers quantitative data and qualitative variables, to propose a multi-dimensional decision framework based on the nested probabilistic linguistic term sets. Under the framework, xgboost algorithm, one of the machine learning methods, is conducted to capture the importance of attributes by using the historical data, and further calculate the attribute weights. The constrained parametric approach is used to establish membership functions of linguistic variables, and then get the objective probabilities in the linguistic model, so that we can obtain a scientific decision matrix. A case study concerning the ranking of bank credit is applied to present the proposed decision framework, and the process of making a rational decision. According to the comparative analysis, the proposed framework is flexible and the result is stable. Managers and policymakers determine the attribute weights by real data and choose the suitable decision method for a certain application. The framework provides an opportunity for capturing, integrating, analyzing data, and interpreting linguistic variables in the model to consider uncertain and cognitive decision at the both theoretical and practical levels.

关键词： Multi-dimensional decision xgboost algorithm Nested probabilistic linguistic term sets Constrained parametric approach Bank credit

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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

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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Integrated framework to integrate Spark-based big data analytics and for health monitoring and recommendation in sports using xgboost algorithm

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SOFT COMPUTING 2023年第2期28卷 1585-1608页

作者： Zhao, Yin Ramos, Ma. Finipina Li, Bin Southwest Med Univ Sch Phys Educ Studies Sect Luzhou 646000 Sichuan Peoples R China Jose Rizal Univ Jose Rizal Univ Grad Sch Mandaluyong 1552 Philippines Southwest Med Univ Sch Phys Educ Luzhou 646000 Sichuan Peoples R China

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people's attention to their health, creating a demand for better medical services. However, China's ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the xgboost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the xgboost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, xgboost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the xgboost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores

关键词： Big data Spark Data mining xgboost algorithm Sports medical integration Service system construction

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A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the xgboost algorithm

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2023年第1期23卷 1-10页

作者： Chen, Rui Zhang, Shengfa Li, Jie Guo, Dongwei Zhang, Weijun Wang, Xiaoying Tian, Donghua Qu, Zhiyong Wang, Xiaohua Cangzhou Cent Hosp Refined Management Off Cangzhou Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Natl Populat Heath Data Ctr Beijing Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China Beijing Normal Univ Sch Social Dev & Publ Policy Beijing Peoples R China

BackgroundThe incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients' *** total of 18,195 ischemic stroke patients' electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1-7 days, 8-14 days and more than 14 days. After preprocessing the data and feature selection, the xgboost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the xgboost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature *** with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke

关键词： Ischemic stroke xgboost algorithm Length of hospital stay (LOS) Machine learning (ML) model

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