Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method

2020 ◽  
Vol 195 ◽  
pp. 105536
Author(s):  
Jiun-Chi Huang ◽  
Yi-Chun Tsai ◽  
Pei-Yu Wu ◽  
Yu-Hui Lien ◽  
Chih-Yi Chien ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Random Forest ◽  
Support Vector Regression ◽  
Linear Model ◽  
Predictive Modeling ◽  
Ensemble Method ◽  
Support Vector ◽  
Lasso Regression

scholarly journals Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 47-58 ◽  
Author(s):  
Akram Farhadi ◽  
Joshua Chern ◽  
Daniel Hirsh ◽  
Tod Davis ◽  
Mingyoung Jo ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Intracranial Pressure ◽  
Random Forest ◽  
Time Series Data ◽  
Brain Structures ◽  
Series Data ◽  
Support Vector ◽  
Lasso Regression ◽  
Dynamic Features ◽  
Threatening Condition

Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to forecast ICP episodes in children, 30 min in advance, by using the dynamic characteristics of continuous intracranial pressure, vitals and medications during the last two hours. The correlation between physiological parameters, including blood pressure, respiratory rate, heart rate and the ICP, is analyzed. Linear regression, Lasso regression, support vector machine and random forest algorithms are used to forecast the next 30 min of the recorded ICP. Finally, dynamic features are created based on vitals, medications and the ICP. The weak correlation between blood pressure and the ICP (0.2) is reported. The Root-Mean-Square Error (RMSE) of the random forest model decreased from 1.6 to 0.89% by using the given medication variables in the last two hours. The random forest regression gave an accurate model for the ICP forecast with 0.99 correlation between the forecast and experimental values.

Application of Combined Prediction Model Based on Core and Coritivity Theory in Continuous Blood Pressure Prediction

2021 ◽  
Vol 24 ◽  
Author(s):  
Kai Zhou ◽  
Zhixiang Yin ◽  
Fei Guo ◽  
Jiasi Li
Keyword(s):  
Blood Pressure ◽  
Random Forest ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Estimation Error ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Random Forest Regression ◽  
Support Vector Regression Model ◽  
Model Based

Background and Objective: Blood pressure is vital evidence for clinicians to predict diseases and check the curative effect of diagnosis and treatment. To further improve the prediction accuracy of blood pressure, this paper proposes a combined prediction model of blood pressure based on coritivity theory and photoplethysmography. Method: First of all, we extract eight features of photoplethysmogram, followed by using eight machine learning prediction algorithms such as K-nearest neighbor, classification and regression trees and random forest to predict systolic blood pressure. Secondly, aiming at the problem of sub-model selection of combination forecasting model, from the point of graph theory, we construct an undirected network graph G, the results of each single prediction model constitute a vertex set. If the maximum mutual information coefficient between vertices is greater than or equal to 0.69, the vertices are connected by edges. The maximum core of graph G is a submodel of the combinatorial model. Results: According to the definition of core and coritivity, the maximum core of G is random forest regression and Gaussian kernel support vector regression model. The results show that the SDP estimation error of the combined prediction model based on random forest regression and Gaussian kernel support vector regression is 3.56 ±5.28mmhg, which is better than other single models and meets the AAMI standards. Conclusion: The combined model determined by core and coritivity has higher prediction performance for blood pressure.

scholarly journals Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application

2018 ◽  
Vol 11 (6) ◽  
pp. 3717-3735 ◽  
Author(s):  
Alessandro Bigi ◽  
Michael Mueller ◽  
Stuart K. Grange ◽  
Grazia Ghermandi ◽  
Christoph Hueglin
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Low Cost ◽  
Multivariate Linear Regression ◽  
Pollution Level ◽  
Support Vector ◽  
Non Linear ◽  
Linear Algorithms ◽  
Urban Sites

Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE  <  5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10 ppb for Random Forest and 15 ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

Wine Evaluation Modeling Based on Lasso and Support Vector Regression

2017 ◽  
Vol 21 (6) ◽  
pp. 998-1008
Author(s):  
Yanyun Yao ◽  
Bing Xu ◽  
Jinghui He ◽  
◽  
Keyword(s):  
Support Vector Regression ◽  
Nonlinear Effect ◽  
Evaluation System ◽  
Evaluation Model ◽  
Digital Simulation ◽  
Objective Evaluation ◽  
Support Vector ◽  
Lasso Regression ◽  
Wine Quality ◽  
Training Samples

Wine consumption is gaining popularity, and significant attention has been given to its quality. In the present paper, an objective evaluation model along with a reliability test via Lasso and nonlinear effect test via support vector regression (SVR) is proposed. The digital simulation is finished with the experimental data obtained from the A problem of CUMCM-2012 (China Undergraduate Mathematical Contest in Modeling in 2012). The results of Lasso regression show that the wine quality mainly depends upon eight physicochemical indicators. Further research results of SVR imply that with several training samples, a good evaluation can be realized, denoting that our model based on Lasso SVR can significantly reduce the costs of measurement and appraisal. Compared to other relevant articles, this paper builds an objective and credible wine evaluation system where the physicochemical indicators and the latent nonlinear effect are considered. Moreover, the evaluation costs are taken into account.

scholarly journals Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

2021 ◽  
Author(s):  
Ewerthon Dyego de Araújo Batista ◽  
Wellington Candeia de Araújo ◽  
Romeryto Vieira Lira ◽  
Laryssa Izabel de Araújo Batista
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Support Vector

Dengue é um problema de saúde pública no Brasil, os casos da doença voltaram a crescer na Paraíba. O boletim epidemiológico da Paraíba, divulgado em agosto de 2021, informa um aumento de 53% de casos em relação ao ano anterior. Técnicas de Machine Learning (ML) e de Deep Learning estão sendo utilizadas como ferramentas para a predição da doença e suporte ao seu combate. Por meio das técnicas Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Long ShortTerm Memory (LSTM) e Convolutional Neural Network (CNN), este artigo apresenta um sistema capaz de realizar previsões de internações causadas por dengue para as cidades Bayeux, Cabedelo, João Pessoa e Santa Rita. O sistema conseguiu realizar previsões para Bayeux com taxa de erro 0,5290, já em Cabedelo o erro foi 0,92742, João Pessoa 9,55288 e Santa Rita 0,74551.

Partitioning of daily evapotranspiration using a modified shuttleworthwallace model, random Forest and support vector regression, for a cabbage farmland

2021 ◽  
Author(s):  
Han Li ◽  
Han Chen ◽  
Jinhui Jeanne Huang ◽  
Edward McBean ◽  
Jiawei Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Support Vector Regression ◽  
Carbon Dioxide Concentration ◽  
Support Vector ◽  
Soil Parameters ◽  
Observation Data ◽  
Source Modeling ◽  
Area Index ◽  
Vegetation Height ◽  
Precision Irrigation

&lt;p&gt;Prediction of vegetation transpiration (T) is of increasing importance in water resources management and agricultural practices, in particular to facilitate precision irrigation. Traditional evapotranspiration (ET) partitioning dual source modeling requires an extensive array of ground-level parameters and needs model correction and calibration to attain model certainty. In response, a quick and low-cost method is described to predict T using artificial intelligence (AI) modeling based on meteorological factors, status of crop growth factors and soil parameters. This study compares Random Forest (RF) and Support Vector Regression (SVR) in building AI models using three years (2014&amp;#8211;2017) of continuous high-resolution monitoring data in a cabbage farmland.Input data included air temperature (Ta), solar radiation (Ra), relative humidity (RH), vapor pressure deficit(VPD), wind speed (Ws), soil moisture (SM), vegetation height (H), and leaf area index (LAI). The results show that soil surface resistance calculations by Monte Carlo iterative method and vegetation stomatal resistance calculations and carbon dioxide concentration and emission, improve performance of the original Shuttleworth&amp;#8211;Wallace(S-W) model. In addition, the AI model indicates Ta and Ra are essential inputs for both model types. When there are sufficient observation data, or only lacking soil and vegetation data, the RF model is recommended for use. When there are only limited data or lack of critical Ta and Ra data, the SVR model is the preferred model. Scientific guidance is provided for agriculture precision irrigation, indicating which AI model can best estimate T and water demand for irrigation planning and water management.&lt;/p&gt;

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