Source discrimination of mine water based on the random forest method

Yang, Zhenwei; Lv, Hang; Zhao, Xu; Wang, Xinyi

doi:10.1038/s41598-022-24037-4

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…Five common machine learning methods, namely Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Logistic regression (LR), and Back propagation (BP) neural network were used to model the data in this study (19)(20)(21)(22)(23).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Yang,

Wang,

Fang

et al. 2024

Front. Neurol.

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ObjectivesWilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods.MethodsThe study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms.ResultsIn this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur.ConclusionsTo sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Yang,

Wang,

Fang

et al. 2024

Front. Neurol.

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“…Subsequently, n classification features (n≤N) are randomly selected from the total N features within each sample set to facilitate full node splitting of decision trees, thus generating M decision trees. Consequently, the category of new samples is determined through a majority voting process among the outcomes obtained from all M decision trees 15 .…”

Section: Methodsmentioning

confidence: 99%

Prediction of glass chemical composition and type identification based on machine learning algorithms

Chen

Zhang

et al. 2023

Preprint

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Glass was a significant symbol of early trade exchanges on the Silk Road. However, the glass artifacts were susceptible to environmental influences when buried underground, leading to weathering and changes in their chemical composition. Analyzing the chemical composition of ancient glass was conducive to evaluate weathering status and propose the measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learnings were used. Results indicated a significant correlation (p < 0.05) was only observed between surface weathering and the glass types (high-potassium and lead-barium). Based on the random forest and logistic regression, the primary chemical components that signifying glass types and weathering status were determined by PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented superior capability for identifying glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates great potential of machine learning for glass chemical component estimation and glass type and weathering status identification providing technical guidance for the appraisal of ancient glass artifacts.

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“…Subsequently, n classification features (n ≤ N) are randomly selected from the total N features within each sample set to facilitate full node splitting of decision trees, thus generating M decision trees. Consequently, the category of new samples is determined through a majority voting process among the outcomes obtained from all M decision trees [28].…”

Section: Random Forest Modelmentioning

confidence: 99%

“…category of new samples is determined through a majority voting process among the outcomes obtained from all M decision trees [28].…”

Section: Random Forest Modelmentioning

confidence: 99%

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chen,

Xu,

Zhang

et al. 2024

Applied Sciences

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Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

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Source discrimination of mine water based on the random forest method

Cited by 10 publications

References 21 publications

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Prediction of glass chemical composition and type identification based on machine learning algorithms

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

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