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   Articles with "xgboost model" as a keyword



Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study

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recommendations! Published in 2019 at "Acta Geophysica"

DOI: 10.1007/s11600-019-00268-4

Abstract: Ground vibration is one of the most undesirable effects induced by blasting operations in open-pit mines, and it can cause damage to surrounding structures. Therefore, predicting ground vibration is important to reduce the environmental effects… read more here.

Keywords: pit; open pit; peak particle; xgboost model ... See more keywords
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Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia.

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recommendations! Published in 2020 at "Journal of hazardous materials"

DOI: 10.1016/j.jhazmat.2020.123492

Abstract: Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial intelligence (AI) models include overfitting, normalization, validation against classical AI… read more here.

Keywords: prediction using; study; model; heavy metal ... See more keywords

Quantifying momentum and influencing factors of tennis players using the XGBoost model

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-02465-2

Abstract: Momentum can directly or indirectly affect a tennis player’s mentality and the trajectory of the game, thereby changing the outcome of the match. The article provides a clear quantitative description of the concept of momentum… read more here.

Keywords: quantifying momentum; momentum; xgboost model; using xgboost ... See more keywords

Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-97878-4

Abstract: Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepatectomy,… read more here.

Keywords: hepatectomy liver; liver failure; model; xgboost model ... See more keywords

Wavelet-Enhanced Hybrid LSTM-XGBoost Model for Predicting Time Series Containing Unpredictable Events

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3556540

Abstract: Accurate electricity consumption forecasting is essential for effective power management, especially in the presence of unpredictable events that disrupt typical consumption patterns. Using the COVID-19 pandemic as a case study for such unpredictable events, this… read more here.

Keywords: lstm xgboost; model; hybrid lstm; xgboost model ... See more keywords

Snow Depth Downscaling Retrieval Based on Spatial-Environment XGBoost Model: A Case Study of the Arid Region of Northwest China

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recommendations! Published in 2025 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2025.3597950

Abstract: As a crucial component of the cryosphere, snow exhibits high sensitivity to climate change and exerts a significant influence on the hydrological cycle and ecological framework. Snow depth (SD) is one of the important information… read more here.

Keywords: land; snow depth; model; xgboost model ... See more keywords

Predictive model and risk analysis for diabetic retinopathy using machine learning: a retrospective cohort study in China

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recommendations! Published in 2021 at "BMJ Open"

DOI: 10.1136/bmjopen-2021-050989

Abstract: Objective Aiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset. Design Retrospective study based on a large sample and a high dimensional database. Setting A… read more here.

Keywords: risk; machine learning; diabetic retinopathy; model ... See more keywords

An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise

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recommendations! Published in 2019 at "Mathematical Problems in Engineering"

DOI: 10.1155/2019/8503252

Abstract: Sales forecasting is even more vital for supply chain management in e-commerce with a huge amount of transaction data generated every minute. In order to enhance the logistics service experience of customers and optimize inventory… read more here.

Keywords: border commerce; commerce; sales forecasting; model ... See more keywords
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Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

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recommendations! Published in 2021 at "Computational and Mathematical Methods in Medicine"

DOI: 10.1155/2021/2577375

Abstract: Acute lymphoblastic leukemia (ALL) is the most common type of pediatric malignancy which accounts for 25% of all pediatric cancers. It is a life-threatening disease which if left untreated can cause death within a few… read more here.

Keywords: classification; lymphoblastic leukemia; inception; model ... See more keywords

IRESpy: an XGBoost model for prediction of internal ribosome entry sites

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recommendations! Published in 2019 at "BMC Bioinformatics"

DOI: 10.1186/s12859-019-2999-7

Abstract: BackgroundInternal ribosome entry sites (IRES) are segments of mRNA found in untranslated regions that can recruit the ribosome and initiate translation independently of the 5′ cap-dependent translation initiation mechanism. IRES usually function when 5′ cap-dependent… read more here.

Keywords: ribosome entry; entry sites; model; xgboost model ... See more keywords

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

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recommendations! Published in 2022 at "Journal of Medical Internet Research"

DOI: 10.2196/38082

Abstract: Background Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF… read more here.

Keywords: xgboost model; machine learning; mortality; model ... See more keywords