Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)

Background: As sepsis is one of the life-threatening diseases, predicting sepsis with high accuracy could help save lives. Methods: Efficiency and accuracy of predicting sepsis can be enhanced through optimal feature selection. In this work, a support vector machine model is proposed to automatically predict a patient’s risk of sepsis based on physiological data collected from the ICU. Results: The support vector machine algorithm that uses the extracted features has a great impact on sepsis prediction, which yields the accuracy of 0.73. Conclusion: Predicting sepsis can be accurately performed using the main vital signs and support vector machine.

Download Full-text

An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽

10.3390/sym13020212 ◽

2021 ◽

Vol 13 (2) ◽

pp. 212

Author(s):

Yu-Wei Liu ◽

Huan Feng ◽

Heng-Yi Li ◽

Ling-Ling Li

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Output Power ◽

Input Data ◽

Support Vector Machine Model ◽

Weather Conditions ◽

Clean Energy ◽

Support Vector ◽

Machine Model ◽

Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

Download Full-text

Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

Medical & Biological Engineering & Computing ◽

10.1007/s11517-020-02299-2 ◽

2021 ◽

Vol 59 (4) ◽

pp. 825-839

Author(s):

Mukul Singh ◽

Shrey Bansal ◽

Sakshi Ahuja ◽

Rahul Kumar Dubey ◽

Bijaya Ketan Panigrahi ◽

...

Keyword(s):

Support Vector Machine ◽

Transfer Learning ◽

Computerized Tomography ◽

Support Vector Machine Model ◽

Support Vector ◽

Machine Model ◽

Scan Data ◽

Tomography Scan

Download Full-text

Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis-Stewart model and hybrid support vector machine model with whale optimization algorithm

Agricultural Water Management ◽

10.1016/j.agwat.2021.106799 ◽

2021 ◽

Vol 249 ◽

pp. 106799

Author(s):

Jing Zheng ◽

Junliang Fan ◽

Fucang Zhang ◽

Lifeng Wu ◽

Yufeng Zou ◽

...

Keyword(s):

Support Vector Machine ◽

Optimization Algorithm ◽

Support Vector Machine Model ◽

Whale Optimization Algorithm ◽

Support Vector ◽

Machine Model ◽

Whale Optimization

Download Full-text

IL-8, MSPa, MIF, FGF-9, ANG-2 and AgRP collection were identified for the diagnosis of colorectal cancer based on the support vector machine model

Cell Cycle ◽

10.1080/15384101.2021.1903208 ◽

2021 ◽

pp. 1-11

Author(s):

Mingfu Cui ◽

Yanan Zhao ◽

Zuocong Zhang ◽

Yang Zhao ◽

Songyun Han ◽

...

Keyword(s):

Colorectal Cancer ◽

Support Vector Machine ◽

Support Vector Machine Model ◽

Support Vector ◽

Machine Model

Download Full-text

A Modified Support Vector Machine model for Credit Scoring

International Journal of Computational Intelligence Systems ◽

10.1080/18756891.2010.9727742 ◽

2010 ◽

Vol 3 (6) ◽

pp. 797-804 ◽

Cited By ~ 3

Author(s):

Xiaoyong Liu ◽

Hui Fu ◽

Weiwei Lin

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Model ◽

Credit Scoring ◽

Support Vector ◽

Machine Model

Download Full-text

Forecast of Urban Rail Transit Passenger Flow in Holidays Based on Support Vector Machine Model

2020 5th International Conference on Electromechanical Control Technology and Transportation (ICECTT) ◽

10.1109/icectt50890.2020.00133 ◽

2020 ◽

Author(s):

Gaoxiang Zhou ◽

Jinjin Tang

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Model ◽

Urban Rail Transit ◽

Support Vector ◽

Rail Transit ◽

Machine Model ◽

Passenger Flow ◽

Urban Rail

Download Full-text

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.291-294.2164 ◽

2013 ◽

Vol 291-294 ◽

pp. 2164-2168 ◽

Cited By ~ 2

Author(s):

Li Tian ◽

Qiang Qiang Wang ◽

An Zhao Cao

Keyword(s):

Support Vector Machine ◽

Heuristic Algorithm ◽

Support Vector Machine Model ◽

Loss Rate ◽

Heuristic Algorithms ◽

Support Vector ◽

Machine Model ◽

Rate Prediction ◽

Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

Download Full-text