Evaluation of influencing factors on tea production based on random forest regression and mean impact value

Chen, Yihui; Li, Minjie

doi:10.17221/399/2018-agricecon

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…In essence, MIV evaluates the significance of each data feature by calculating its mean impact value over multiple iterations. 17 , 18 With MIV's insights, we can better understand the importance of each feature, simplifying feature selection and model tuning. The filtered data is then used to train various machine learning models, focusing on predicting the GOS scores and the duration of a patient's hospital stay.…”

Section: Methodsmentioning

confidence: 99%

Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

Pan,

Fang,

Zhu

et al. 2023

DIGITAL HEALTH

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Objective To investigate the mean impact value (MIV) method for discerning the most efficacious input variables for the machine learning (ML) model. Subsequently, various ML algorithms are harnessed to formulate a more accurate predictive model that can forecast both the prognosis and the length of hospital stay for patients suffering from traumatic brain injury (TBI). Design Retrospective cohort study. Participants The study retrospectively accrued data from 1128 cases of patients who sought medical intervention at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University, within the timeframe spanning from May 2017 to May 2022. Methods We performed a retrospective analysis of patient data obtained from the Neurosurgery Center of the Second Hospital of Anhui Medical University, covering the period from May 2017 to May 2022. Following meticulous data filtration and partitioning, 70% of the data were allocated for model training, while the remaining 30% served for model evaluation. During the construction phase of the ML models, a gamut of 11 independent variables—including, but not limited to, in-hospital complications and patient age—were utilized as input variables. Conversely, the length of stay (LOS) and the Glasgow Outcome Scale (GOS) scores were designated as output variables. The model architecture was initially refined through the MIV methodology to identify optimal input variables, whereupon five distinct predictive models were constructed, encompassing support vector regression (SVR), convolutional neural networks (CNN), backpropagation (BP) neural networks, artificial neural networks (ANN) and logistic regression (LR). Ultimately, SVR emerged as the most proficient predictive model and was further authenticated through an external dataset obtained from the First Hospital of Anhui Medical University. Results Upon incorporating the optimal input variables as ascertained through MIV, it was observed that the SVR model exhibited remarkable predictive prowess. Specifically, the Mean Absolute Percentage Error (MAPE) of the SVR model in predicting the GOS score in the test dataset is only 6.30%, and the MAPE in the external validation set is only 7.61%. In terms of predicting hospitalization time, the accuracy of the test and external validation sets were 9.28% and 7.91%, respectively. This error indicator is significantly lower than the error of other prediction models, thus proving the excellent efficacy and clinical reliability of the MIV-optimized SVR model. Conclusion This study unequivocally substantiates that the incorporation of MIV for selecting optimal input variables can substantially augment the predictive accuracy of machine learning models. Among the models examined, the MIV-SVR model emerged as the most accurate and clinically applicable, thereby rendering it highly conducive for future clinical decision-making processes.

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Section: Methodsmentioning

confidence: 99%

Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

Pan,

Fang,

Zhu

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

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“…The selection of input features has a direct impact on the accuracy and computational efficiency of the model. Currently, the Mean Impact Value (MIV) algorithm is considered one of the best methods for achieving data dimensionality reduction when combined with neural networks [27][28][29]. The MIV algorithm measures the importance of independent variables to the dependent variable by comparing the absolute value of the MIV of each feature.…”

Section: Mean Impact Valuementioning

confidence: 99%

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

Zhang,

Ma,

Cheng

et al. 2024

Processes

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Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately.

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“…Tea is one of the most popular beverages in the world, and the public consumes tea and its derivatives second only to water [1]. China is a major country in tea production and consumption, with tea planting areas accounting for approximately 60% of the global tea planting area [2,3]. Most tea gardens adopt a single planting mode with only tea trees or a single variety, which leads to an overly simple ecosystem structure in the tea garden and affects the soil properties, structure, and nutrients [4].…”

Section: Introductionmentioning

confidence: 99%

Effects of Multiple-Metal-Compound Contamination on the Soil Microbial Community in Typical Karst Tea Plantations

Huang,

Wang,

et al. 2023

Forests

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In this study, the effects of pollution levels and heavy metal pollution on soil microbial diversity in karst tea plantations are reported. Four tea plantations from plateau hills, under forests, by lakesides and on steep slopes in the South China karst were used as research regions. Soil samples were taken from these tea plantations, the soil heavy metals Cd, Cr, Pb, Zn, Ni and Cu were tested using inductively coupled plasma-mass spectrometry, and Hg and As were tested via atomic fluorescence spectrometry. The soil microbes were analyzed via high-throughput sequencing technology. Heavy metal pollution was evaluated via the single factor index and pollution load index, and the correlation between soil heavy metals and the microbial community was analyzed via SPSS 18.0 and Canoco 5.0 software. The results showed that the studied tea plantation soils were greatly polluted by the heavy metals, Cd and Hg, to a low to moderate degree. The comprehensive pollution of multiple heavy metals occurred only in lakeside tea plantations, in which pollution reached a low degree. It is also suggested that Hg and Cd were the major contributors, followed by Cu. The soil microbial diversity in soil samples from lakeside tea plantations was the highest; however, the discrepancy in its dominant species composition was also the highest. When the pollution load index was close to 0.6, the microbial diversity decreased sharply. Afterward, the diversity and heterogeneity generally gently increased, and the dominant composition was more obvious. These results reveal that the impact of heavy metal pollution on soil microbial diversity was not very distinct, but the impact on the dominant microbial community composition was obvious. In addition, the heavy metals, Cd, Hg and Cu, were the key factors that impacted the soil microbial community composition.

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Evaluation of influencing factors on tea production based on random forest regression and mean impact value

Cited by 8 publications

References 26 publications

Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

Effects of Multiple-Metal-Compound Contamination on the Soil Microbial Community in Typical Karst Tea Plantations

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