Prediction Model of Bone Marrow Infiltration in Patients with Malignant Lymphoma Based on Logistic Regression and XGBoost Algorithm

Huang, Yongfen; Chen, Can; Miao, Yuqing

doi:10.1155/2022/9620780

Cited by 2 publications

(2 citation statements)

References 29 publications

(29 reference statements)

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“…XGBoost is an iterative decision tree algorithm, which uses residuals to improve the model. First, XGBoost supports parallel computing, second, it also supports regularization, which prevents model overfitting (Huang et al, 2022). Although the model is highly accurate, it platform whose value ranges from 2.33 to 18.07.…”

Section: Xgboost Regressionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Machine Learning Algorithms

Eghrari

Delavar²,

Zare³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Land subsidence (LS) is one of the most challenging natural disasters that has potential consequences such as damage to infrastructures and buildings, creating sinkholes, and leading to soil destruction. To mitigate the damages caused by LS, it is necessary to determine the LS-prone areas. In this paper, LS susceptibility was assessed for Kashan Plain in Iran using Random Forest (RF) and XGBoost machine learning algorithms. For the susceptibility analysis, twelve influential factors including elevation, slope, aspect, curvature, topographic wetness index (TWI), groundwater drawdown (GWD), normalized difference vegetation index (NDVI), distance to stream (DtS), distance to road (DtR), distance to fault (DtF), lithology, and land use were taken into account. 291 LS points were used in this study which was divided into two parts of 70% and 30% for training and testing the models, respectively. The prediction power of the models and their produced LS susceptibility maps (LSSMs) were validated using the Root Mean Square Error (RMSE), R-Squared (R2), and Mean Absolute Error (MAE) values. The results showed that the XGBoost had a higher R² equal to 0.9032 compared to that of the RF which was equal to 0.8355. XGBoost model had an RMSE equal to 0.3764 cm compared to that of the RF model which was equal to 0.4906 cm. MAE for the XGBoost model was 0.1217 cm and for the RF model was 0.3050 cm. Therefore, the achieved results proved that XGBoost had better performance in this research for predicting LS values based on the measured ones.

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Section: Xgboost Regressionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Machine Learning Algorithms

Eghrari

Delavar²,

Zare³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In a study conducted by [12], when predicting the spinal cord infiltration model in patients with malignant lymphoma, logistic regression and XGBoost were used. XGBoost is a better model with an AUC value of 0.844 compared to logistic regression.…”

Section: Introductionmentioning

confidence: 99%

Pre-Processing Data on Multiclass Classification of Anemia and Iron Deficiency With the Xgboost Method

Nurrahman

Wijayanto

Wigena

et al. 2023

BAREKENG: J. Math. & App.

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Anemia and iron deficiency are health problems in Indonesia and globally. In Multiclass Classification, data problems often occur, such as missing data, too many variables, and unbalanced data. Then pre-processing data will be carried out using MissForest imputation, Boruta featuring selection, and SMOTE to help improve the performance of the classification model in predicting a particular class. After the data pre-processing process is carried out, classification modeling will be carried out using the XGBoost algorithm. It was found that when pre-processing the data could improve the performance of the model in predicting multiclass classification for cases of anemia and iron deficiency in women in Indonesia by 0.815 for the accuracy value and 0.9693 for the AUC value

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Prediction Model of Bone Marrow Infiltration in Patients with Malignant Lymphoma Based on Logistic Regression and XGBoost Algorithm

Cited by 2 publications

References 29 publications

Land Subsidence Susceptibility Mapping Using Machine Learning Algorithms

Land Subsidence Susceptibility Mapping Using Machine Learning Algorithms

Pre-Processing Data on Multiclass Classification of Anemia and Iron Deficiency With the Xgboost Method

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