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Journal articles on the topic 'XGBOOST PREDICTION MODEL'

Author: Grafiati

Published: 11 September 2023

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1

Zhao, Haolei, Yixian Wang, Xian Li, Panpan Guo, and Hang Lin. "Prediction of Maximum Tunnel Uplift Caused by Overlying Excavation Using XGBoost Algorithm with Bayesian Optimization." Applied Sciences 13, no. 17 (2023): 9726. http://dx.doi.org/10.3390/app13179726.

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The uplifting behaviors of existing tunnels due to overlying excavations are complex and non-linear. They are contributed to by multiple factors, and therefore, they are difficult to be accurately predicted. To address this issue, an extreme gradient boosting (XGBoost) prediction model based on Bayesian optimization (BO), namely, BO-XGBoost, was developed specifically for assessing the tunnel uplift. The modified model incorporated various factors such as an engineering design, soil types, and site construction conditions as input parameters. The performance of the BO-XGBoost model was compare

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Gu, Xinqin, Li Yao, and Lifeng Wu. "Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms." Sustainability 15, no. 16 (2023): 12333. http://dx.doi.org/10.3390/su151612333.

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Quantification of water carbon fluxes in rice paddies and analysis of their causes are essential for agricultural water management and carbon budgets. In this regard, two tree-based machine learning models, which are extreme gradient boosting (XGBoost) and random forest (RF), were constructed to predict evapotranspiration (ET), net ecosystem carbon exchange (NEE), and methane flux (FCH4) in seven rice paddy sites. During the training process, the k-fold cross-validation algorithm by splitting the available data into multiple subsets or folds to avoid overfitting, and the XGBoost model was used

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Liu, Jialin, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu, and Ke Li. "Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model." PLOS ONE 16, no. 2 (2021): e0246306. http://dx.doi.org/10.1371/journal.pone.0246306.

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Purpose The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models. Methods We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), an

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Wang, Jun, Wei Rong, Zhuo Zhang, and Dong Mei. "Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China." Wireless Communications and Mobile Computing 2022 (March 19, 2022): 1–14. http://dx.doi.org/10.1155/2022/8005493.

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The bond market is an important part of China’s capital market. However, defaults have become frequent in the bond market in recent years, and consequently, the default risk of Chinese credit bonds has become increasingly prominent. Therefore, the assessment of default risk is particularly important. In this paper, we utilize 31 indicators at the macroeconomic level and the corporate microlevel for the prediction of bond defaults, and we conduct principal component analysis to extract 10 principal components from them. We use the XGBoost algorithm to analyze the importance of variables and ass

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Gu, Zhongyuan, Miaocong Cao, Chunguang Wang, Na Yu, and Hongyu Qing. "Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model." Sustainability 14, no. 16 (2022): 10421. http://dx.doi.org/10.3390/su141610421.

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The extreme gradient boosting (XGBoost) ensemble learning algorithm excels in solving complex nonlinear relational problems. In order to accurately predict the surface subsidence caused by mining, this work introduces the genetic algorithm (GA) and XGBoost integrated algorithm model for mining subsidence prediction and uses the Python language to develop the GA-XGBoost combined model. The hyperparameter vector of XGBoost is optimized by a genetic algorithm to improve the prediction accuracy and reliability of the XGBoost model. Using some domestic mining subsidence data sets to conduct a model

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Kang, Leilei, Guojing Hu, Hao Huang, Weike Lu, and Lan Liu. "Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction." Journal of Advanced Transportation 2020 (August 14, 2020): 1–16. http://dx.doi.org/10.1155/2020/3247847.

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In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be

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Wang, Wenle, Wentao Xiong, Jing Wang, et al. "A User Purchase Behavior Prediction Method Based on XGBoost." Electronics 12, no. 9 (2023): 2047. http://dx.doi.org/10.3390/electronics12092047.

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With the increasing use of electronic commerce, online purchasing users have been rapidly rising. Predicting user behavior has therefore become a vital issue based on the collected data. However, traditional machine learning algorithms for prediction require significant computing time and often produce unsatisfactory results. In this paper, a prediction model based on XGBoost is proposed to predict user purchase behavior. Firstly, a user value model (LDTD) utilizing multi-feature fusion is proposed to differentiate between user types based on the available user account data. The multi-feature

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Oubelaid, Adel, Abdelhameed Ibrahim, and Ahmed M. Elshewey. "Bridging the Gap: An Explainable Methodology for Customer Churn Prediction in Supply Chain Management." Journal of Artificial Intelligence and Metaheuristics 4, no. 1 (2023): 16–23. http://dx.doi.org/10.54216/jaim.040102.

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Customer churn prediction is a critical task for businesses aiming to retain their valuable customers. Nevertheless, the lack of transparency and interpretability in machine learning models hinders their implementation in real-world applications. In this paper, we introduce a novel methodology for customer churn prediction in supply chain management that addresses the need for explainability. Our approach take advantage of XGBoost as the underlying predictive model. We recognize the importance of not only accurately predicting churn but also providing actionable insights into the key factors d

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Liu, Yuan, Wenyi Du, Yi Guo, Zhiqiang Tian, and Wei Shen. "Identification of high-risk factors for recurrence of colon cancer following complete mesocolic excision: An 8-year retrospective study." PLOS ONE 18, no. 8 (2023): e0289621. http://dx.doi.org/10.1371/journal.pone.0289621.

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Background Colon cancer recurrence is a common adverse outcome for patients after complete mesocolic excision (CME) and greatly affects the near-term and long-term prognosis of patients. This study aimed to develop a machine learning model that can identify high-risk factors before, during, and after surgery, and predict the occurrence of postoperative colon cancer recurrence. Methods The study included 1187 patients with colon cancer, including 110 patients who had recurrent colon cancer. The researchers collected 44 characteristic variables, including patient demographic characteristics, bas

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He, Wenwen, Hongli Le, and Pengcheng Du. "Stroke Prediction Model Based on XGBoost Algorithm." International Journal of Applied Sciences & Development 1 (December 13, 2022): 7–10. http://dx.doi.org/10.37394/232029.2022.1.2.

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In this paper, individual sample data randomly measured are preprocessed, for example, outliers values are deleted and the characteristics of the samples are normalized to between 0 and 1. The correlation analysis approach is then used to determine and rank the relevance of stroke characteristics, and factors with poor correlation are discarded. The samples are randomly split into a 70% training set and a 30% testing set. Finally,the random forest model and XGBoost algorithm combined with cross-validation and grid search method are implemented to learn the stroke characteristics. The accuracy

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Shin, Juyoung, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, and Hun-Sung Kim. "Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness." Journal of Personalized Medicine 12, no. 11 (2022): 1899. http://dx.doi.org/10.3390/jpm12111899.

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The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embeddi

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Wu, Kehe, Yanyu Chai, Xiaoliang Zhang, and Xun Zhao. "Research on Power Price Forecasting Based on PSO-XGBoost." Electronics 11, no. 22 (2022): 3763. http://dx.doi.org/10.3390/electronics11223763.

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With the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In order to improve the prediction accuracy of the power price, this paper proposes a power price prediction method based on PSO optimization of the XGBoost model, which optimizes eight main parameters of the XGBoost model through particle swarm optimization to improve the prediction accuracy of the XGBoo

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Xiong, Shuai, Zhixiang Liu, Chendi Min, Ying Shi, Shuangxia Zhang, and Weijun Liu. "Compressive Strength Prediction of Cemented Backfill Containing Phosphate Tailings Using Extreme Gradient Boosting Optimized by Whale Optimization Algorithm." Materials 16, no. 1 (2022): 308. http://dx.doi.org/10.3390/ma16010308.

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Unconfined compressive strength (UCS) is the most significant mechanical index for cemented backfill, and it is mainly determined by traditional mechanical tests. This study optimized the extreme gradient boosting (XGBoost) model by utilizing the whale optimization algorithm (WOA) to construct a hybrid model for the UCS prediction of cemented backfill. The PT proportion, the OPC proportion, the FA proportion, the solid concentration, and the curing age were selected as input variables, and the UCS of the cemented PT backfill was selected as the output variable. The original XGBoost model, the

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Wang, Yu, Li Guo, Yanrui Zhang, and Xinyue Ma. "Research on CSI 300 Stock Index Price Prediction Based On EMD-XGBoost." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 72–77. http://dx.doi.org/10.54097/fcis.v3i1.6027.

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The combination of artificial intelligence techniques and quantitative investment has given birth to various types of price prediction models based on machine learning algorithms. In this study, we verify the applicability of machine learning fused with statistical method models through the EMD-XGBoost model for stock price prediction. In the modeling process, specific solutions are proposed for overfitting problems that arise. The stock prediction model of machine learning fused with statistical learning was constructed from an empirical perspective, and an XGBoost algorithm model based on em

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Yang, Tian. "Sales Prediction of Walmart Sales Based on OLS, Random Forest, and XGBoost Models." Highlights in Science, Engineering and Technology 49 (May 21, 2023): 244–49. http://dx.doi.org/10.54097/hset.v49i.8513.

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The technique of estimating future sales levels for a good or service is known as sales forecasting. The corresponding forecasting methods range from initially qualitative analysis to later time series methods, regression analysis and econometric models, as well as machine learning methods that have emerged in recent decades. This paper compares the different performances of OLS, Random Forest and XGBoost machine learning models in predicting the sales of Walmart stores. According to the analysis, XGBoost model has the best sales forecasting ability. In the case of logarithmic sales, R2 of the

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Li, Kunluo. "A Sales Prediction Method Based on XGBoost Algorithm Model." BCP Business & Management 36 (January 13, 2023): 367–71. http://dx.doi.org/10.54691/bcpbm.v36i.3487.

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Reasonable and accurate sales forecasting is an important issue for large chain stores. Forecasting short- and long-term product sales helps companies develop marketing strategies and inventory turnover plans. In today's ever-changing business environment, the application of artificial intelligence technology allows for more efficient processing of large amounts of data while taking into account many external factors such as the climate, consumer patterns, and financial situation. An XGBoost linear regression model for the Kaggle competition was trained using the dataset of Ecuadorian Favorita

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Yang, Hao, Jiaxi Li, Siru Liu, Xiaoling Yang, and Jialin Liu. "Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation." JMIR Medical Informatics 10, no. 6 (2022): e36958. http://dx.doi.org/10.2196/36958.

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Background Hypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed. Objective The aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes. Methods We used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, a

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Syafrudin, Muhammad, Ganjar Alfian, Norma Latif Fitriyani, et al. "A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting." Mathematics 8, no. 9 (2020): 1590. http://dx.doi.org/10.3390/math8091590.

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving t

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Li, Weihong, and Xiujuan Xu. "Ensemble learning algorithm - research analysis on the management of financial fraud and violation in listed companies." Decision Making: Applications in Management and Engineering 6, no. 2 (2023): 722–33. http://dx.doi.org/10.31181/dmame622023785.

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In recent years, despite the strict "zero tolerance" crackdown on the financial fraud and violation behavior of listed companies, the cases of financial fraud, revenue and profit overstatement, and suspected fraud have continued to be exposed. This study first established a financial fraud index system and used the XGBoost algorithm to construct a prediction model for financial fraud and violations of listed companies. The indicators were selected and input into the model. A dataset was obtained for experiments. The XGBoost algorithm was compared with two other algorithms. The receiver operato

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Rasaizadi, Arash, and Seyedehsan Seyedabrishami. "Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow." Journal of Advanced Transportation 2022 (June 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/3198636.

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By predicting and informing the future of traffic through intelligent transportation systems, there is more readiness to avoid traffic congestion. In this study, an ensemble learning process is proposed to predict the hourly traffic flow. First, three base models, including K-nearest neighbors, random forest, and recurrent neural network, are trained. Predictions of base models are given to the XGBoost stacking model and bagged average to determine the final prediction. Two groups of models predict traffic flow of short-term and mid-term future. In mid-term models, predictor features are cycli

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Lu, Xin, Cai Chen, RuiDan Gao, and ZhenZhen Xing. "Prediction of High-Speed Traffic Flow around City Based on BO-XGBoost Model." Symmetry 15, no. 7 (2023): 1453. http://dx.doi.org/10.3390/sym15071453.

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The prediction of high-speed traffic flow around the city is affected by multiple factors, which have certain particularity and difficulty. This study devised an asymmetric Bayesian optimization extreme gradient boosting (BO-XGBoost) model based on Bayesian optimization for the spatiotemporal and multigranularity prediction of high-speed traffic flow around a city. First, a traffic flow dataset for a ring expressway was constructed, and the data features were processed based on the original data. The data were then visualized, and their spatiotemporal distribution exhibited characteristics suc

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Zhang, Chao, Yihang Zhao, and Huiru Zhao. "A Novel Hybrid Price Prediction Model for Multimodal Carbon Emission Trading Market Based on CEEMDAN Algorithm and Window-Based XGBoost Approach." Mathematics 10, no. 21 (2022): 4072. http://dx.doi.org/10.3390/math10214072.

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Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant stakeholders, and can also provide a reference for policy makers. However, the time interval for the CTP is one day, resulting in a relatively small sample size of data available for predictions. When dealing with small sample data, deep learning algorithms can trade only a small improvement in prediction accuracy at the expense of efficiency and computing time. In contrast, fine-grained configurations of traditional model inputs and parameters often perform no less well than deep learning algor

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Tang, Jinjun, Lanlan Zheng, Chunyang Han, Fang Liu, and Jianming Cai. "Traffic Incident Clearance Time Prediction and Influencing Factor Analysis Using Extreme Gradient Boosting Model." Journal of Advanced Transportation 2020 (June 9, 2020): 1–12. http://dx.doi.org/10.1155/2020/6401082.

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Accurate prediction and reliable significant factor analysis of incident clearance time are two main objects of traffic incident management (TIM) system, as it could help to relieve traffic congestion caused by traffic incidents. This study applies the extreme gradient boosting machine algorithm (XGBoost) to predict incident clearance time on freeway and analyze the significant factors of clearance time. The XGBoost integrates the superiority of statistical and machine learning methods, which can flexibly deal with the nonlinear data in high-dimensional space and quantify the relative importan

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Huang, Yongfen, Can Chen, and Yuqing Miao. "Prediction Model of Bone Marrow Infiltration in Patients with Malignant Lymphoma Based on Logistic Regression and XGBoost Algorithm." Computational and Mathematical Methods in Medicine 2022 (June 28, 2022): 1–7. http://dx.doi.org/10.1155/2022/9620780.

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Objective. The prediction model of bone marrow infiltration (BMI) in patients with malignant lymphoma (ML) was established based on the logistic regression and the XGBoost algorithm. The model’s prediction efficiency was evaluated. Methods. A total of 120 patients diagnosed with ML in the department of hematology from January 2018 to January 2021 were retrospectively selected. The training set ( n = 84 ) and test set ( n = 36 ) were randomly divided into 7 : 3, and logistic regression and XGBoost algorithm models were constructed using the training set data. Predictors of BMI were screened bas

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Thongprayoon, Charat, Pattharawin Pattharanitima, Andrea G. Kattah, et al. "Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury." Journal of Clinical Medicine 11, no. 21 (2022): 6264. http://dx.doi.org/10.3390/jcm11216264.

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Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operat

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Zhang, Ping, Rongqin Wang, and Nianfeng Shi. "IgA Nephropathy Prediction in Children with Machine Learning Algorithms." Future Internet 12, no. 12 (2020): 230. http://dx.doi.org/10.3390/fi12120230.

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Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of renal failure. IgAN prediction in children with machine learning algorithms has been rarely studied. We retrospectively analyzed the electronic medical records from the Nanjing Eastern War Zone Hospital, chose eXtreme Gradient Boosting (XGBoost), random forest (RF), CatBoost, support vector machines (SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM) models in order to predict the probability that the patient would not reach or reach end-stage renal di

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Feng, Dachun, Bing Zhou, Shahbaz Gul Hassan, et al. "A Hybrid Model for Temperature Prediction in a Sheep House." Animals 12, no. 20 (2022): 2806. http://dx.doi.org/10.3390/ani12202806.

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Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Aiming at the randomness and empirical problem of parameter selection of the traditional single extreme Gradient boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. First, PCA is used to screen

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Yuan, Yufei, Ruoran Wang, Mingyue Luo, et al. "A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer." BioMed Research International 2022 (October 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/8501819.

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Background. Liver metastasis (LM) is an independent risk factor that affects the prognosis of patients with ovarian cancer; however, there is still a lack of prediction. This study developed a limit gradient enhancement (XGBoost) to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer, thereby improving prediction efficiency. Patients and Methods. Data of patients diagnosed with ovarian cancer in the Surveillance, Epidemiology, and Final Results (SEER) database from 2010 to 2015 were retrospectively collected. The XGBoost algorithm was used to establish a lung me

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Chen, Mujun, Xiangmei Meng, Guangming Kan, et al. "Predicting the Sound Speed of Seafloor Sediments in the East China Sea Based on an XGBoost Algorithm." Journal of Marine Science and Engineering 10, no. 10 (2022): 1366. http://dx.doi.org/10.3390/jmse10101366.

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Based on the acoustic and physical data of typical seafloor sediment samples collected in the East China Sea, this study on the super parameter selection and contribution of the characteristic factors of the machine learning model for predicting the sound speed of seafloor sediments was conducted using the eXtreme gradient boosting (XGBoost) algorithm. An XGBoost model for predicting the sound speed of seafloor sediments was established based on five physical parameters: density (ρ), water content (w), void ratio (e), sand content (S), and average grain size (Mz). The results demonstrated that

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Gopatoti, Anandbabu. "A novel metaheuristic prediction approach for COVID-19 cases using XGBoost algorithm." International Journal of Scientific Methods in Intelligence Engineering Networks 01, no. 01 (2023): 85–93. http://dx.doi.org/10.58599/ijsmien.2023.1108.

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COVID-19 prediction is of great importance to build stronger government prevention and control of the global pandemic. This pandemic has a devastating impact on people and their lives. Many industries are suffering and are struggling to overcome unexpected pandemic challenges. Therefore, it is extremely important to develop up to date and practicable models for time series prediction of COVID-19 that would give promising results. In this paper, we build a model for predicting daily confirmed n w cases for the time series data of Europe by applying the Extreme Gradient Boosting (XGBoost) algori

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Chen, Yuhuan, and Yingqing Jiang. "Construction of Prediction Model of Deep Vein Thrombosis Risk after Total Knee Arthroplasty Based on XGBoost Algorithm." Computational and Mathematical Methods in Medicine 2022 (January 25, 2022): 1–6. http://dx.doi.org/10.1155/2022/3452348.

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Objective. Based on the XGBoost algorithm, the prediction model of the risk of deep vein thrombosis (DVT) in patients after total knee arthroplasty (TKA) was established, and the prediction performance was compared. Methods. A total of 100 patients with TKA from January 2019 to December 2020 were retrospectively selected as the study subjects and randomly divided into a training set ( n = 60 ) and a test set ( n = 40 ). The training set data was used to construct the XGBoost algorithm prediction model and to screen the predictive factors of postoperative DVT in TKA patients. The prediction eff

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Yang, Zhao, Yifan Wang, Jie Li, Liming Liu, Jiyang Ma, and Yi Zhong. "Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods." Complexity 2020 (October 26, 2020): 1–11. http://dx.doi.org/10.1155/2020/6309272.

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This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new pre

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Oh, Sejong, Yuli Park, Kyong Jin Cho, and Seong Jae Kim. "Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation." Diagnostics 11, no. 3 (2021): 510. http://dx.doi.org/10.3390/diagnostics11030510.

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The aim is to develop a machine learning prediction model for the diagnosis of glaucoma and an explanation system for a specific prediction. Clinical data of the patients based on a visual field test, a retinal nerve fiber layer optical coherence tomography (RNFL OCT) test, a general examination including an intraocular pressure (IOP) measurement, and fundus photography were provided for the feature selection process. Five selected features (variables) were used to develop a machine learning prediction model. The support vector machine, C5.0, random forest, and XGboost algorithms were tested f

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Xu, Bing, Youcheng Tan, Weibang Sun, Tianxing Ma, Hengyu Liu, and Daguo Wang. "Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model." Sustainability 15, no. 6 (2023): 5201. http://dx.doi.org/10.3390/su15065201.

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The uniaxial compressive strength of rock is one of the important parameters characterizing the properties of rock masses in geotechnical engineering. To quickly and accurately predict the uniaxial compressive strength of rock, a new SSA-XGBoost optimizer prediction model was produced to predict the uniaxial compressive strength of 290 rock samples. With four parameters, namely, porosity (n,%), Schmidt rebound number (Rn), longitudinal wave velocity (Vp, m/s), and point load strength (Is(50), MPa) as input variables and uniaxial compressive strength (UCS, MPa) as the output variables, a predic

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Harriz, Muhammad Alfathan, Nurhaliza Vania Akbariani, Harlis Setiyowati, and Handri Santoso. "Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction." Jambura Journal of Informatics 5, no. 1 (2023): 1–6. http://dx.doi.org/10.37905/jji.v5i1.18814.

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This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuabl

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Liu, Linxiang, Yuan Nie, Qi Liu, and Xuan Zhu. "A Practical Model for Predicting Esophageal Variceal Rebleeding in Patients with Hepatitis B-Associated Cirrhosis." International Journal of Clinical Practice 2023 (August 3, 2023): 1–11. http://dx.doi.org/10.1155/2023/9701841.

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Background. Variceal rebleeding is a significant and potentially life-threatening complication of cirrhosis. Unfortunately, currently, there is no reliable method for stratifying high-risk patients. Liver stiffness measurements (LSM) have been shown to have a predictive value in identifying complications associated with portal hypertension, including first-time bleeding. However, there is a lack of evidence to confirm that LSM is reliable in predicting variceal rebleeding. The objective of our study was to evaluate the ability of generating a extreme gradient boosting (XGBoost) algorithm model

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Guo, Jiang, Chen Zhang, Shoudong Xie, and Yi Liu. "Research on the Prediction Model of Blasting Vibration Velocity in the Dahuangshan Mine." Applied Sciences 12, no. 12 (2022): 5849. http://dx.doi.org/10.3390/app12125849.

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In order to improve the prediction accuracy of blast vibration velocity, the model for predicting the peak particle velocity of blast vibration using the XGBoost (Extreme Gradient Boosting) method is improved, and the EWT–XGBoost model is established to predict the peak particle velocity of blast vibration by combining it with the EWT (Empirical Wavelet Transform) method. Calculate the relative error and root mean square error between the predicted value and measured value of each test sample, and compare the prediction performance of the EWT–XGBoost model with the original model. There is a l

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Yuan, Jianming. "Predicting Death Risk of COVID-19 Patients Leveraging Machine Learning Algorithm." Applied and Computational Engineering 8, no. 1 (2023): 186–90. http://dx.doi.org/10.54254/2755-2721/8/20230122.

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The first instance of COVID-19 was found in Wuhan, China, which mainly caused damage to human body in the form of respiratory diseases. In this study, an XGBoost prediction model was put forward according to the analysis on age, pneumonia, diabetes, and other attributes in the dataset, which was employed to estimate the COVID-19 patients' risk of death. In this study, a lot of preprocessing was carried out on the dataset, such as deleting null values in the dataset. In addition, there are strong correlation between sex, pnueumonia and death probability. In this study, XGBoost, CatBoost, logist

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Li, Mingguang, Runyi Huang, and Yumiao Yang. "Short-term wind speed prediction based on combinatorial prediction model." Highlights in Science, Engineering and Technology 60 (July 25, 2023): 274–82. http://dx.doi.org/10.54097/hset.v60i.10534.

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Improving the accuracy of wind speed forecast can increase wind power generation and better achieve wind energy grid connection. Therefore, a two-stage wind speed prediction model based on Ensemble Empirical Modal Decomposition (EEMD) and the combination prediction of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), eXtreme Gradient Boosting (XGBOOST), Gate Recurrent Unit (GRU), Temporal Convolutional Network (TCN) is proposed. First, the original wind speed series is separated into Intrinsic Mode Functions (IMFs) using EEMD. Then, RNN, LSTM, XGBOOST, GRU, TCN multiple prediction

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Li, Xiangcheng, Jialong Wang, Zhirui Geng, Yang Jin, and Jiawei Xu. "Short-term Wind Power Prediction Method Based on Genetic Algorithm Optimized XGBoost Regression Model." Journal of Physics: Conference Series 2527, no. 1 (2023): 012061. http://dx.doi.org/10.1088/1742-6596/2527/1/012061.

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Abstract In order to solve the problem of accuracy and rapidity of short-term prediction of wind power output, the eXtreme Gradient Boosting (XGBoost) regression model is used in this paper to predict wind power output. For the models commonly used at the present stage, such as Long Short Term Memory (LSTM), random forest and ordinary XGBoost model, the modelling time is long, and the accuracy is not enough. In this paper, a genetic algorithm (GA) is introduced to improve the accuracy and speed of prediction of the XGBoost regression model. Firstly, the learning rate of the XGBoost model is op

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Kuthe, Annaji, Chaitanya Bhake, Vaibhav Bhoyar, Aman Yenurkar, Vedant Khandekar, and Ketan Gawale. "Water Quality Prediction Using Machine Learning." International Journal of Computer Science and Mobile Computing 12, no. 4 (2023): 52–59. http://dx.doi.org/10.47760/ijcsmc.2023.v12i04.006.

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Different toxins have been imperiling water quality over the past decades. As a result, foreseeing and modeling water quality have gotten to be basic to minimizing water contamination. This inquiry has created a classification calculation to foresee the water quality classification (WQC). The WQC is classified based on the water quality file (WQI) from 7 parameters in a dataset utilizing Back Vector Machine (SVM) and Extraordinary Gradient Boosting (XGBoost). The comes about from the proposed model can precisely classify the water quality based on their features. The inquire about result illus

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Meng, Delin, Jun Xu, and Jijun Zhao. "Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost." PLOS ONE 16, no. 12 (2021): e0261629. http://dx.doi.org/10.1371/journal.pone.0261629.

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Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analy

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Lin, Xiaoxuan, Lixin Chen, Defu Zhang, et al. "Prediction of Surgical Approach in Mitral Valve Disease by XGBoost Algorithm Based on Echocardiographic Features." Journal of Clinical Medicine 12, no. 3 (2023): 1193. http://dx.doi.org/10.3390/jcm12031193.

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In this study, we aimed to develop a prediction model to assist surgeons in choosing an appropriate surgical approach for mitral valve disease patients. We retrospectively analyzed a total of 143 patients who underwent surgery for mitral valve disease. The XGBoost algorithm was used to establish a predictive model to decide a surgical approach (mitral valve repair or replacement) based on the echocardiographic features of the mitral valve apparatus, such as leaflets, the annulus, and sub-valvular structures. The results showed that the accuracy of the predictive model was 81.09% in predicting

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Ding, Chao, Yuwen Guo, Qinqin Mo, and Jin Ma. "Prediction Model of Postoperative Severe Hypocalcemia in Patients with Secondary Hyperparathyroidism Based on Logistic Regression and XGBoost Algorithm." Computational and Mathematical Methods in Medicine 2022 (July 25, 2022): 1–7. http://dx.doi.org/10.1155/2022/8752826.

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Objective. A predictive model was established based on logistic regression and XGBoost algorithm to investigate the factors related to postoperative hypocalcemia in patients with secondary hyperparathyroidism (SHPT). Methods. A total of 60 SHPT patients who underwent parathyroidectomy (PTX) in our hospital were retrospectively enrolled. All patients were randomly divided into a training set ( n = 42 ) and a test set ( n = 18 ). The clinical data of the patients were analyzed, including gender, age, dialysis time, body mass, and several preoperative biochemical indicators. The multivariate logi

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Moore, Alexander, and Max Bell. "XGBoost, A Novel Explainable AI Technique, in the Prediction of Myocardial Infarction: A UK Biobank Cohort Study." Clinical Medicine Insights: Cardiology 16 (January 2022): 117954682211336. http://dx.doi.org/10.1177/11795468221133611.

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We wanted to assess if “Explainable AI” in the form of extreme gradient boosting (XGBoost) could outperform traditional logistic regression in predicting myocardial infarction (MI) in a large cohort. Two machine learning methods, XGBoost and logistic regression, were compared in predicting risk of MI. The UK Biobank is a population-based prospective cohort including 502 506 volunteers with active consent, aged 40 to 69 years at recruitment from 2006 to 2010. These subjects were followed until end of 2019 and the primary outcome was myocardial infarction. Both models were trained using 90% of t

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Xu, Jialing, Jingxing He, Jinqiang Gu, et al. "Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 15, 2022): 637–45. http://dx.doi.org/10.46300/9106.2022.16.79.

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Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Extreme gradient boosting (XGBoost) is used for feature extraction to improve prediction accuracy. Alibaba stock is taken as the research object, using XGBoost to optimize its characteristic factors, and training the optimized characteristic variables with WGAN-GP. We compare the prediction results of WGAN-GP model and classical time series prediction models, long short term

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Jin, Deyan. "Risk Prediction Method of Obstetric Nursing Based on Data Mining." Contrast Media & Molecular Imaging 2022 (August 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/5100860.

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Obstetric nursing is not only complex but also prone to risks, which can have adverse effects on hospitals. Improper handling of existing risks in obstetric care can lead to enormous harm to patients and families. Therefore, it is necessary to pay attention to the risks of obstetric nursing, especially to predict the risks in a timely manner, and take effective measures to prevent them in time, so as to achieve the purpose of allowing patients to recover as soon as possible. Data mining has powerful forecasting function, so this paper proposes to combine the data-mining-based support vector ma

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Dai, Hongbin, Guangqiu Huang, Huibin Zeng, and Fan Yang. "PM2.5 Concentration Prediction Based on Spatiotemporal Feature Selection Using XGBoost-MSCNN-GA-LSTM." Sustainability 13, no. 21 (2021): 12071. http://dx.doi.org/10.3390/su132112071.

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With the rapid development of China’s industrialization, air pollution is becoming more and more serious. Predicting air quality is essential for identifying further preventive measures to avoid negative impacts. The existing prediction of atmospheric pollutant concentration ignores the problem of feature redundancy and spatio-temporal characteristics; the accuracy of the model is not high, the mobility of it is not strong. Therefore, firstly, extreme gradient lifting (XGBoost) is applied to extract features from PM2.5, then one-dimensional multi-scale convolution kernel (MSCNN) is used to ext

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Narvekar, Aditya, and Debashis Guha. "Bankruptcy prediction using machine learning and an application to the case of the COVID-19 recession." Data Science in Finance and Economics 1, no. 2 (2021): 180–95. http://dx.doi.org/10.3934/dsfe.2021010.

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<abstract> <p>Bankruptcy prediction is an important problem in finance, since successful predictions would allow stakeholders to take early actions to limit their economic losses. In recent years many studies have explored the application of machine learning models to bankruptcy prediction with financial ratios as predictors. This study extends this research by applying machine learning techniques to a quarterly data set covering financial ratios for a large sample of public U.S. firms from 1970–2019. We find that tree-based ensemble methods, especially XGBoost, can achieve a high

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Kartina Diah Kusuma Wardani and Memen Akbar. "Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost)." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 4 (2023): 824–31. http://dx.doi.org/10.29207/resti.v7i4.4651.

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Diabetes results from impaired pancreas function as a producer of insulin and glucagon hormones, which regulate glucose levels in the blood. People with diabetes today are not only experienced adults, but pre-diabetes has been identified since the age of children and adolescents. Early prediction of diabetes can make it easier for doctors and patients to intervene as soon as possible so that the risk of complications can be reduced. One of the uses of medical data from diabetes patients is used to produce a model that can be used by medical staff to predict and identify diabetes in patients. V

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