Prediction Model for Diagnosis of Kawasaki Disease Using iTRAQ-Based Analysis

A quick prediction method may help confirm the diagnosis of Kawasaki disease (KD), and reduce the risk of coronary artery lesions. The purpose of this study was to evaluate potential candidate diagnostic serum proteins in KD using isobaric tagging for relative and absolute quantification (iTRAQ) gel-free proteomics. Ninety two subjects, including 68 KD patients (1.6 ± 1.2 years, M/F 36/32) and 24 fever controls with evident respiratory tract infection (2.1 ± 1.2 years, M/F 13/11) were enrolled. Medical records were reviewed for demographic and laboratory data. The iTRAQ gel-free proteomics was used to screen serum proteins completely and compare the difference between two groups followed by specific validation with ELISA. The candidate proteins and conventional laboratory items were selected for the prediction model of KD diagnosis by support vector machine. Five selected candidate proteins, including protein S100-A8, protein S100-A9, protein S100-A12, neutrophil defensin 1, and alpha-1-acid glycoprotein 1 were identified for developing the prediction model of KD diagnosis. They were used to develop an efficient KD prediction model with an area under receiver operating characteristic (auROC) value of 0.92 (95% confidence interval: 0.84, 0.98). These protein biomarkers were significantly correlated with the conventional laboratory items as follows: C-reactive protein, glutamic pyruvic transaminase, white blood count, platelet, segment and hemoglobin. These conventional laboratory items were used to develop a prediction model of KD diagnosis with an auROC value of 0.88 (95% confidence interval: 0.80, 0.96). Our result demonstrated that the prediction model with combined five selected candidate protein levels may be a good diagnostic tool of KD. Further prediction model with combined six conventional laboratory data is also an acceptable alternative method for KD diagnosis.

Download Full-text

Research on Combined Prediction Model for Busy Telephone Traffic

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.789 ◽

2014 ◽

Vol 610 ◽

pp. 789-796

Author(s):

Jiang Bao Li ◽

Zhen Hong Jia ◽

Xi Zhong Qin ◽

Lei Sheng ◽

Li Chen

Keyword(s):

Prediction Model ◽

Prediction Accuracy ◽

Low Frequency ◽

Prediction Method ◽

Pso Algorithm ◽

Frequency Component ◽

Least Square ◽

High Frequency Component ◽

Support Vector ◽

Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

Download Full-text

Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219862412 ◽

2019 ◽

Vol 42 (1) ◽

pp. 94-103 ◽

Cited By ~ 1

Author(s):

Weigang Bao ◽

Hua Wang ◽

Jie Chen ◽

Bo Zhang ◽

Peng Ding ◽

...

Keyword(s):

Prediction Model ◽

Support Vector Regression ◽

Life Prediction ◽

Feature Fusion ◽

Prediction Method ◽

Support Vector ◽

Isometric Mapping ◽

Slewing Bearing ◽

Time Frequency ◽

Life Prediction Model

The monitoring data of slewing bearing is massive. In order to establish accurate life prediction model from complex vibration signal of slewing bearing, a life prediction method based on manifold learning and fuzzy support vector regression (SVR) is proposed. Firstly, the multiple features are extracted from time domain and time-frequency domain. Then isometric mapping (ISOMAP) is used to reduce high-dimensional features to low-dimensional features that can reflect degeneration of slewing bearing well. Finally, the fuzzy SVR is used to predict the life degradation trend of slewing bearing. The results show that: (1) Multi-feature fusion after ISOMAP can obtain more comprehensive degradation indicator. (2) The complexity of the life prediction model is simplified and the real-time life degradation trend of slewing bearing can be well predicted by fuzzy SVR, so it is very suitable to predict life degradation trend of slewing bearing based on massive data well. The time of prediction on average is reduced by 72.7%. The mean absolute error (MAE) and root mean square error (RMSE) of prediction are reduced by 73% and 59% respectively compared with traditional methods. The accuracy of prediction is greatly improved.

Download Full-text

Adaptive Kalman Filter Based Aircraft Ground Icing Thickness Prediction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.562-564.1660 ◽

2012 ◽

Vol 562-564 ◽

pp. 1660-1667

Author(s):

Zhi Wei Xing ◽

Hui Zhang ◽

Zhun Ren

Keyword(s):

Kalman Filter ◽

Prediction Model ◽

Prediction Method ◽

Theory And Practice ◽

Filter Method ◽

Support Vector ◽

Aircraft Icing ◽

Adaptive Kalman Filter ◽

Thickness Prediction ◽

Kalman Filter Method

The nonlinear dynamics model is used to describe the change of aircraft icing thickness and icing deformation accelerations is viewed as dynamic noise in this paper. Then, a dynamic prediction model of aircraft icing thickness is established with the theory of adaptive kalman filter. And the adaptive kalman filter method based aircraft icing thickness prediction model is employed to forecast aircraft ground icing thickness and compared with support vector machine, BP neural network prediction method. The result of the instance simulation and analysis indicates that the adaptive kalman filter method based aircraft icing thickness prediction posed in this paper is reliable, simple and rapid, and the model has high prediction precision which can realize real-time tracking and prediction and has definite value of both theory and practice.

Download Full-text

A Novel Prediction Method of Dynamic Wall Pressure for Silos Based on Support Vector Machine

Advances in Civil Engineering ◽

10.1155/2020/4865628 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Hanhua Yu ◽

Zhijun Xu ◽

Tingting Liu ◽

Fang Yuan

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Mechanical Characteristics ◽

Lateral Pressure ◽

Prediction Method ◽

Wall Pressure ◽

Model Fit ◽

Support Vector ◽

Simulation Methods ◽

Grid Search Method

The physical properties and mechanical characteristics of storage materials are significantly different from those of ordinary solids and liquids. The distribution of dynamic wall pressure during silo discharge is quite complicated. Considering the nonlinear relationship between the factors which affect the dynamic lateral pressure of silos, a prediction method of dynamic wall pressure for silos based on support vector machine (SVM) is proposed here, and furthermore, the modified grid search method (GSM) is incorporated in obtaining the optimal support vector machine parameters to improve the accuracy of the prediction. Comparing the results of the proposed prediction model with the results of experiment methods and simulation methods, it can be found that the SVM prediction model shows high accuracy and high generalization ability, and the prediction results of the model fit well with the results of experiment and simulation methods. The proposed method can provide reference for the prediction of the dynamic wall pressure of silos.

Download Full-text

Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA-LSSVM-KDE

Mathematical Problems in Engineering ◽

10.1155/2019/1649086 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Zhao Xue-hua ◽

Miao Xu-juan ◽

Zhang Zhen-gang ◽

Hao Zheng

Keyword(s):

Prediction Model ◽

Transmission Line ◽

Prediction Interval ◽

High Reliability ◽

Prediction Method ◽

Principal Component ◽

Support Vector ◽

Investment Planning ◽

Project Investment ◽

Project Cost

In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.

Download Full-text

IMPROVED ITERATIVE PREDICTION FOR MULTIPLE STOP ARRIVAL TIME USING A SUPPORT VECTOR MACHINE

Transport ◽

10.3846/16484142.2012.692710 ◽

2012 ◽

Vol 27 (2) ◽

pp. 158-164 ◽

Cited By ~ 7

Author(s):

Chang-Jiang Zheng ◽

Yi-Hua Zhang ◽

Xue-Jun Feng

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Arrival Time ◽

Prediction Models ◽

Prediction Method ◽

Support Vector ◽

Dynamic Algorithm ◽

Proposed Model ◽

Speed Prediction ◽

Two Stages

The paper presents an improved iterative prediction method for bus arrival time at multiple downstream stops. A multiple-stop prediction model includes two stages. At the first stage, an iterative prediction model is developed, which includes a single stop prediction model for arrival time at the immediate downstream stop and an average bus speed prediction model on further segments. The two prediction models are constructed with a support vector machine (SVM). At the second stage, a dynamic algorithm based on the Kalman filter is developed to enhance prediction accuracy. The proposed model is assessed with reference to data collected on transit route No 23 in Dalian city, China. The obtained results show that the improved iterative prediction model seems to be a powerful tool for predicting multiple stop arrival time.

Download Full-text

A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

Remote Sensing ◽

10.3390/rs14010030 ◽

2021 ◽

Vol 14 (1) ◽

pp. 30

Author(s):

Boyi Li ◽

Adu Gong ◽

Tingting Zeng ◽

Wenxuan Bao ◽

Can Xu ◽

...

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Support Vector Regression ◽

Prediction Method ◽

Historical Earthquakes ◽

Support Vector ◽

Learning Methods ◽

Support Vector Regression Model ◽

Machine Learning Methods ◽

Earthquake Casualty

The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.

Download Full-text

Serum proteins may facilitate the identification of Kawasaki disease and promote in vitro neutrophil infiltration

Scientific Reports ◽

10.1038/s41598-020-72695-z ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sung-Chou Li ◽

Kuo-Wang Tsai ◽

Lien-Hung Huang ◽

Ken-Pen Weng ◽

Kuang-Jen Chien ◽

...

Keyword(s):

Kawasaki Disease ◽

High Performance ◽

Serum Proteins ◽

Prediction Method ◽

In Vitro Study ◽

Neutrophil Infiltration ◽

Protein Biomarkers ◽

S100a12 Protein ◽

Fast Prediction

Abstract Kawasaki disease (KD) usually affects the children younger than 5 years of age and subsequently causes coronary artery lesions (CALs) without timely identification and treatment. Developing a robust and fast prediction method may facilitate the timely diagnosis of KD, significantly reducing the risk of CALs in KD patients. The levels of inflammatory serum proteins dramatically vary during the onsets of many immune diseases, including in KD. However, our understanding of their pathogenic roles in KD is behind satisfaction. The purpose of this study was to evaluate candidate diagnostic serum proteins and the potential mechanism in KD using iTRAQ gel-free proteomics. We enrolled subjects and conducted iTRAQ gel-free proteomics to globally screen serum proteins followed by specific validation with ELISA. Further in vitro leukocyte trans-endothelial model was also applied to investigate the pathogenesis roles of inflammatory serum proteins. We identified six KD protein biomarkers, including Protein S100-A8 (S100A8), Protein S100-A9 (S100A9), Protein S100-A12 (S100A12), Peroxiredoxin-2 (PRDX2), Neutrophil defensin 1 (DEFA1) and Alpha-1-acid glycoprotein 1 (ORM1). They enabled us to develop a high-performance KD prediction model with an auROC value of 0.94, facilitating the timely identification of KD. Further assays concluded that recombinant S100A12 protein treatment activated neutrophil surface adhesion molecules responsible for adhesion to endothelial cells. Therefore, S100A12 promoted both freshly clinically isolated neutrophils and neutrophil-like cells to infiltrate through the endothelial layer in vitro. Finally, the antibody against S100A12 may attenuate the infiltration promoted by S100A12. Our result demonstrated that evaluating S100A8, S100A9, S100A12, PRDX2, DEFA1 and ORM1 levels may be a good diagnostic tool of KD. Further in vitro study implied that S100A12 could be a potential therapeutic target for KD.

Download Full-text

Online prediction and control method for compensation regulation value in grinding processes

Advances in Mechanical Engineering ◽

10.1177/1687814019837105 ◽

2019 ◽

Vol 11 (3) ◽

pp. 168781401983710

Author(s):

Peng Zheng ◽

Dong-liang Liu ◽

Xue-hao Tian ◽

Zhan-xin Zhi ◽

Lin-na Zhang

Keyword(s):

Prediction Model ◽

Control Method ◽

Prediction Method ◽

Feedback System ◽

Optimization Method ◽

Support Vector ◽

Machining Accuracy ◽

Grinding Process ◽

Compensation Control ◽

And Control

In any grinding process, compensation regulation value is a crucial factor for maintaining precision during the batch processing of workpieces. Geometric characteristics, buffing allowance, temperature, wheel speed, and workpiece speed are the main factors that affect compensation regulation value in any grinding process. In this article, a novel prediction method for compensation regulation value is proposed based on incremental support vector machine and mixed kernel function. The support vectors for the prediction model are extracted using the convex hull vertex optimization algorithm, and the speed of the operation can be increased effectively. In addition, the parameters of the model are optimized using cross-validation optimization method to improve the accuracy of the prediction model. Then, the feedback control strategy of compensation regulation value for the grinding process is also proposed. Single-factor and multi-factor experiments are implemented respectively using the proposed method. The results verify the feasibility and effectiveness of the proposed method. It is also noted that the machining accuracy is improved significantly in comparison with the machining without prediction and compensation control. Moreover, by applying the prediction compensation control of compensation regulation value to the active measurement and control of the grinding process, a feedback system is formed, and then the intelligentization of the grinding system can be realized.

Download Full-text

Short-Term Power Generation Energy Forecasting Model for Small Hydropower Stations Using GA-SVM

Mathematical Problems in Engineering ◽

10.1155/2014/381387 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 7

Author(s):

Gang Li ◽

Yongjun Sun ◽

Yong He ◽

Xiufeng Li ◽

Qiyu Tu

Keyword(s):

Power Generation ◽

Prediction Model ◽

Small Sample ◽

Support Vector ◽

Forecasting Model ◽

Potential Candidate ◽

Short Term ◽

Small Hydropower ◽

Energy Forecasting ◽

Energy Prediction

Accurate and reliable power generation energy forecasting of small hydropower (SHP) is essential for hydropower management and scheduling. Due to nonperson supervision for a long time, there are not enough historical power generation records, so the forecasting model is difficult to be developed. In this paper, the support vector machine (SVM) is chosen as a method for short-term power generation energy prediction because it shows many unique advantages in solving small sample, nonlinear, and high dimensional pattern recognition. In order to identify appropriate parameters of the SVM prediction model, the genetic algorithm (GA) is performed. The GA-SVM prediction model is tested using the short-term observations of power generation energy in the Yunlong County and Maguan County in Yunnan province. Through the comparison of its performance with those of the ARMA model, it is demonstrated that GA-SVM model is a very potential candidate for the prediction of short-term power generation energy of SHP.

Download Full-text