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LASSO regression and boruta algorithm to explore the relationship between neutrophil percentage to albumin ratio and asthma: results from the NHANES 2001 to 2018

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Clinical and Experimental Medicine 2025年第1期25卷 1-10页

作者： Fu, Yumin Zhao, Jijing Wang, Yunpeng The First People’s Hospital of Jiande Zhejiang Province Hangzhou 311600 China

The present study aims to investigate the relationship between the neutrophil-percentage-to-albumin ratio (NPAR) and asthma using least absolute shrinkage and selection operator (LASSO) regression and boruta algorithm. Based on the National Health and Nutrition Examination Survey database from 2001 to 2018, a total of 31,138 eligible participants were included in this study. The participants were randomly divided into a training cohort and a validation cohort in a 7:3 ratio. LASSO regression and boruta algorithm were applied to the training cohort for assessment, selection of the optimal model, and identification of potential confounding factors. A nomogram prediction model, receiver operating characteristic curve, calibration curve, and decision curve analysis were constructed to evaluate the model’s ability to predict the risk of asthma and its stability. These analyses aim to provide a reference for clinical diagnosis and treatment. The study demonstrated that after adjusting for potential confounding factors, the NPAR was positively correlated with asthma incidence (P < 0.01). The area under the curve for the training set was 0.66 for LASSO regression and 0.64 for boruta algorithm, indicating that LASSO regression exhibited superior performance. Through LASSO regression, 10 variables were selected, including gender, race, smoking status, hypertension, diabetes, cancer, poverty-income ratio, BMI, cardiovascular disease, and age. A nomogram prediction model was constructed based on these predictors. The calibration curve showed good fit between the two groups. A higher NPAR is significantly positively correlated with an increased risk of asthma. © The Author(s) 2025.

关键词： Asthma boruta algorithm LASSO regression NHANES NPAR

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Assessment of the importance of gully erosion effective factors using boruta algorithm and its spatial modeling and mapping using three machine learning algorithms

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GEODERMA 2019年 340卷 55-69页

作者： Amiri, Mandis Pourghasemi, Hamid Reza Ghanbarian, Gholam Abbas Afzali, Sayed Fakhreddin Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran

The Maharloo watershed has witnessed many gullies in the recent due to the specific topo-climatic conditions and man-made activities in that area. The present study is set out to address this issue by producing gully erosion prediction maps via three machine learning algorithms including RF, SVM and BRT in Maharloo watershed, Fars province, Iran. Also, this research attempted to consider the importance of effective factors in the occurrence of gully erosion using boruta algorithm. To this end, gully erosion locations were identified by extensive field surveys as well as the use of already prepared gully raster map of Maharloo watershed. Then, sixteen causative factors of gully erosion such as elevation, slope degree, slope aspect, plan curvature, TWI, distance from rivers, distance from roads, drainage density, lithology, annual mean rainfall, NDVI, land use and some soil characteristics (pH, clay percent, electrical conductivity-EC, and silt percent) were identified and their maps were produced and classified in the GIS. In this study, the relationships among each agent and gully erosion were defined employing the evidential belief function (EBF) algorithm and the weight of each factor's classes was determined. On the other hand, the results of the collinearity test among the factors showed that sand percentage agent had a VIF > 5;therefore, this covariate was removed from the model. Also, the results of the importance of effective factors using boruta algorithm indicated that three factors including land use, distance from river, and clay percent had the most noticeable importance in the occurrence of gully erosion in the study area. Finally, the gully erosion susceptibility maps were produced using the RF, BRT, and SVM models in the R statistical software. The results of machine learning techniques were evaluated employing 30% of unused locations in the modeling process as well as the receiver operating characteristic (ROC) curve. Also, in the current research, t

关键词： Gully erosion Machine learning boruta algorithm Evidential belief function Integrating GIS and R

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Evaluation of boruta algorithm in DDoS detection

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EGYPTIAN INFORMATICS JOURNAL 2023年第1期24卷 27-42页

作者： Farhana, Noor Firdaus, Ahmad Darmawan, Mohd Faaizie Razak, Mohd Faizal Ab Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pekan 26600 Pahang Malaysia Univ Teknol Mara Fac Comp & Math Sci Tapah Perak Malaysia

Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows over time, detecting a Distributed Denial of Service (DDoS) assault is a challenging task. Furthermore, a dataset con-taining a large number of features may degrade machine learning's detection performance. Therefore, in machine learning, it is necessary to prepare a relevant list of features for the training phase in order to obtain good accuracy performance. With far too many possibilities, choosing the relevant feature is com-plicated. This study proposes the boruta algorithm as a suitable approach to achieve accuracy in identi-fying the relevant features. To evaluate the boruta algorithm, multiple classifiers (J48, random forest, naive bayes, and multilayer perceptron) were used so as to determine the effectiveness of the features selected by the the boruta algorithm. The outcomes obtained showed that the random forest classifier had a higher value, with a 100% true positive rate, and 99.993% in the performance measure of accuracy, when compared to other classifiers.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intel-ligence, Cairo University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： DDoS boruta algorithm Machine learning J48 Random forest Na?ve bayes Multilayer perceptron

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Forecasting the Global Horizontal Irradiance based on boruta algorithm and Artificial Neural Networks using a Lower Cost

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第9期11卷 79-92页

作者： Alresheedi, Abdulatif Aoihan Al-Hagery, Mohammed Abdullah Qassim Univ Coll Comp Dept Comp Sci Buraydah Saudi Arabia Qassim Univ Coll Comp BIND Res Grp Buraydah Saudi Arabia

More solar-based electricity generation stations have been established markedly in recent years as new and an important source of renewable energy. That is to ensure a more efficient, reliable integration of solar power to overcome several challenges such as, the future forecasting, the costly equipment in the metrological stations. One of the effective prediction methods is Artificial Neural Networks (ANN) and the boruta algorithm for optimal attributes selection, to train the proposed prediction model to obtain high accurate prediction performance at a lower cost. The precise goal of this research is to predict the Global Horizontal Irradiance (GHI) by building the ANN model. Also, reducing the total number of GHI prediction attributes/features consequently reducing the cost of devices and equipment required to predict this important factor. The dataset applied in this research is real data, collected from 2015-2018 by solar and meteorological stations in KSA. It provided by King Abdullah City for Atomic and Renewable Energy (KA CARE). The findings emphasize the achievement of accurate predictions of solar radiation with a minimum cost, which is considered to be highly important in KSA and all other countries that have a similar environment.

关键词： Global horizontal irradiance artificial neural networks feature selection boruta algorithm cost reduction machine learning

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Prediction Model for in-Stent Restenosis Post-PCI Based on boruta algorithm and Deep Learning: The Role of Blood Cholesterol and Lymphocyte Ratio

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JOURNAL OF MULTIDISCIPLINARY HEALTHCARE 2024年 17卷 4731-4739页

作者： Hou, Ling Su, Ke He, Ting Zhao, Jinbo Li, Yuanhong Hubei Univ Med Cent Hosp Tujia & Miao Autonomous Prefecture Cardiovasc Dis Ctr Enshi Hubei Peoples R China Hubei Univ Med Dept Cent Hosp Tujia & Miao Autonomous Prefecture Shiyan Hubei Peoples R China

Background: Percutaneous coronary intervention (PCI) is the primary treatment for acute myocardial infarction (AMI). However, instent restenosis (ISR) remains a significant limitation to the efficacy of PCI. The cholesterol-to-lymphocyte ratio (CLR), a novel biomarker associated with inflammation and dyslipidemia, may have predictive value for ISR. Deep learning-based models, such as the multilayer perceptron (MLP), can aid in establishing predictive models for ISR using CLR. Methods: A retrospective analysis was conducted on clinical and laboratory data from 1967 patients. The boruta algorithm was employed to identify key features associated with ISR. An MLP model was developed and divided into training and validation sets. Model performance was evaluated using ROC curves and calibration plots. Results: Patients in the ISR group exhibited significantly higher levels of CLR and low-density lipoprotein (LDL) compared to the non-ISR group. The boruta algorithm identified 21 important features for subsequent modeling. The MLP model achieved an AUC of 0.95 on the validation set and 0.63 on the test set, indicating good predictive performance. Calibration plots demonstrated good agreement between predicted and observed outcomes. Feature importance analysis revealed that the number of initial stent implants, hemoglobin levels, Gensini score, CLR, and white blood cell count were significant predictors of ISR. Partial dependence plots (PDP) confirmed CLR as a key predictor for ISR. Conclusion: The CLR, as a biomarker that integrates lipid metabolism and inflammation, shows significant potential in predicting coronary ISR. The MLP model, based on deep learning, demonstrated robust predictive capabilities, offering new insights and strategies for clinical decision-making.

关键词： cholesterol-to-lymphocyte ratio deep learning multilayer perceptron boruta algorithm in-stent restenosis

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Application of the boruta algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating

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ENERGIES 2021年第10期14卷 2779页

作者： Szul, Tomasz Tabor, Sylwester Pancerz, Krzysztof Univ Agr Fac Prod & Power Engn PL-30149 Krakow Poland Szymon Szymonow State Sch Higher Educ Zamosc Dept Technol & Comp Sci PL-22400 Zamosc Poland

Energy prediction used for building heating has attracted particular attention because it is often required in the development of various strategies to improve the energy efficiency of buildings, especially those undergoing thermal improvements. The complexity, dynamics, uncertainty, and nonlinearity of existing building energy systems create a great need for modeling techniques. One of them is machine learning models, which are based on input data consisting of features that describe the objects under study. The data describing actual buildings used to build the model may be characterized by missing values, duplicate or inconsistent features, noise, and outliers. Therefore, an extremely important aspect of the prediction model development effort is the proper selection of features to simplify the prediction of energy consumption for heating. In this connection, the goal was to evaluate the usefulness of a model describing the final energy demand rate for building heating using groups of features describing actual residential buildings undergoing thermal retrofit. The model was created by combining two algorithms: the boruta feature selection algorithm, which prepares conditional variables corresponding to features for a prediction model based on rough set theory (RST). The research was conducted on a group of 109 multi-family buildings from the end of the last century (made in large-panel technology), thermomodernized at the beginning of the 21st century. Evaluation metrics such as MAPE, MBE, CV RMSE, and R-2, which are adopted as statistical calibration standards by ASHRAE, were used to assess the quality of the developed prediction model. The analysis of the obtained results indicated that the model based on RST, based on the features selected by the boruta algorithm, gives a satisfactory prediction quality with a limited number of input variables, and thus allows to predict energy consumption (after thermal improvement) for this type of buildings with high accur

关键词： data selection boruta algorithm building load prediction rough set theory building energy modeling thermal improvement of buildings

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Novel CNN-based transformer integrating boruta algorithm for production prediction modeling and energy saving of industrial processes

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期255卷

作者： Han, Yongming Han, Longkun Shi, Xinwei Li, Jun Huang, Xiaoyi Hu, Xuan Chu, Chong Geng, Zhiqiang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Harvard Univ Harvard Med Sch Dept Biomed Informat Boston MA USA

Industrial process data collected by sensors have characteristics of high dimensionality, non-linearity and dynamics. Consequently, the selection extraction is regarded as a critical part for reducing the dimension of the data and removing irrelevant variables of constructing the production prediction model of industrial processes. Therefore, a novel production prediction model using the boruta algorithm integrating the convolutional neural network-based Transformer (BCT) is proposed in this paper. Primarily, the boruta algorithm maps nonlinear high-dimensional data to a low-dimensional space to select features that are meaningful to the yield of the industrial process. Then, the features are extracted adaptively using a convolutional neural network (CNN), which is encoded based on the transformer layer to learn relevant information about the different representation spaces. Furthermore, a linear layer with highway connections is employed to obtain prediction results. Finally, the BCT method is applied to establish a realistic production prediction model of actual liquefied petroleum gas plants for energy saving. Compared with back propagation neural networks, the radial basis function, the extreme learning machine, and the transformer based on the CNN, the BCT method achieves a state-of-the-art level. Furthermore, the BCT method provides the operation guidance on the actual liquefied petroleum gas (LPG) production process with increasing the LPG yield by 17.21%, which can improve production efficiency and reducing energy consumption.

关键词： Feature extraction boruta algorithm Convolutional neural network Production prediction Energy saving Liquefied Petroleum Gas plant

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Stock Market Prediction Using a Hybrid Approach With boruta and Liquid Neural Network

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Reddy, G. Joshita Sahani, Rahul Kumar Raja, S. P. Vellore Inst Technol Dept Comp Sci & Engn Vellore 632014 India

Globally, one of the most fundamental applications of time-series data is stock market forecasting, where each and every second is crucial and analysis is unpredictable, posing a significant challenge towards predicting its trend. Various research is underway for the analysis of the stock market using various time series algorithms such as long-short-term memory (LSTM), recurrent neural network (RNN). Here, in this article, we have proposed a novel approach of multiple layers including liquid time constant, the linear first order dynamics with nonlinear interlinked gates which improves performance of time series;after which we included liquid neural network and forecasted the results. Then, we enhanced the algorithm using boruta feature selection, where we have selected only the required columns and ranked them as per requirement. Furthermore, for more accurate results, we have selected three different sets of historical data for five large cap sectors (Alphabet, Apple, Blackrock, JP Morgan, and IBM) after which we observed the R-square values along with all the performance evaluation and error evaluation results for all the individual dataset in two phases, i.e., before and after using the boruta feature selection method for 5, 10, and 15 years data, we observed the best results for LSTM (0.9443) and RNN (0.9752). Liquid neural networks, being the most efficient and accurate, gave the best R-square value of 0.9835.

关键词： Stock markets Companies Long short term memory Prediction algorithms Liquids Market research Predictive models Time series analysis Heuristic algorithms Data models boruta algorithm liquid neural networks (LNNs) liquid time constant (LTC) stock market prediction

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Research on implicit emotion recognition and classification in literary works in the context of machine learning

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ALEXANDRIA ENGINEERING JOURNAL 2025年 115卷 577-584页

作者： Zhao, Yiqian Zhang, Yuanshou Weifang Univ Foreign Languages Sch Weifang 261061 Shandong Peoples R China Weifang Univ Sch Math & Stat Weifang 261061 Shandong Peoples R China

Emotion prediction is crucial in areas such as human-computer interaction, consumer experience, and mental health treatment;nonetheless, effectively forecasting emotions is difficult due to various intricate factors. This study tackles these problems by employing the boruta algorithm for feature selection, thereby assuring that only pertinent attributes influence the predictions. The curated dataset was further examined utilizing machine learning models: support vector machine (SVM), K-nearest neighbor (KNN) method and artificial neural network (ANN). The results indicate that SVM attains the greatest accuracy of 0.91, succeeded by ANN at 0.89 and KNN at 0.88, underscoring SVM's appropriateness for this dataset owing to its strong boundary- setting proficiency. Although ANN adeptly accommodates intricate patterns because to its flexibility, KNN's marginally diminished accuracy may result from its susceptibility to class overlap. All models demonstrate robust predicted accuracy, confirming their dependability for emotion classification tasks. The findings indicate that SVM, specifically, may improve applications in user experience, mental health, and AI-facilitated customer interactions, providing significant assistance for data-informed decision-making across many sectors.

关键词： Emotion Classification Machine learning Prediction boruta algorithm

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Exploring the mediating role of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) in the association between obesity and sleep-disordered breathing

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EATING AND WEIGHT DISORDERS-STUDIES ON ANOREXIA BULIMIA AND OBESITY 2025年第1期30卷 1-15页

作者： Cui, Ying Cheng, Ziyi Korea Univ Grad Sch & Transdisciplinary Major Learning Hlth S Grad Sch Dept Publ Hlth Sci 145Anam Ro Seoul South Korea Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL England

PurposeExploring novel mediators affecting the relationship between obesity and sleep-disordered breathing (SDB) is necessary. This study aimed to explore the mediating role of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) in the association between body mass index (BMI) and SDB using data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) *** 7639 participants were included. SDB was defined based on the self-reported frequency of snoring, snorting, or excessive daytime sleepiness. The BMI and NHHR were calculated based on height and weight measurements and laboratory data, respectively. Weighted multivariate logistic and linear regression analyses were conducted to examine the associations, and restricted cubic spline (RCS) analysis was used to assess dose-response relationships. Mediation analysis was performed to evaluate the NHHR's role in the BMI-SDB association. Subgroup analyses were performed to assess differences across various *** symptoms were observed in 51.05% of participants. Higher BMI was significantly associated with increased SDB risk. RCS analysis revealed a nonlinear relationship between BMI and SDB. Subgroup analyses indicated a positive correlation between BMI and SDB was stronger among nonhypertensive participants. NHHR was positively associated with BMI and SDB. Mediation analysis showed that the NHHR explained 5.44-8.12% of the BMI-SDB *** is a critical factor in the risk of SDB, and the NHHR partially mediates this relationship. BMI and cholesterol levels should be managed to mitigate the SDB *** of evidenceLevel V-cross-sectional observational study.

关键词： Sleep-disordered breathing High-density lipoprotein cholesterol ratio Body mass index Restricted cubic spline model boruta algorithm

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