Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree

2018 ◽  
Vol 559 ◽  
pp. 499-509 ◽  
Author(s):  
Salim Heddam ◽  
Ozgur Kisi
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Regression Splines ◽  
Dissolved Oxygen Concentration ◽  
M5 Model Tree ◽  
Adaptive Regression ◽  
Adaptive Regression Splines ◽  
M5 Model

scholarly journals Hybrid Support Vector Machine to Preterm Birth Prediction

2018 ◽  
Vol 8 (2) ◽  
pp. 191
Author(s):  
Noviyanti Santoso ◽  
Sri Pingit Wulandari
Keyword(s):  
Support Vector Machine ◽  
Preterm Birth ◽  
Prediction Model ◽  
Research Area ◽  
Multivariate Adaptive Regression Splines ◽  
Human Reproduction ◽  
Support Vector ◽  
Premature Babies ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Preterm birth is one of the major contributors to perinatal and neonatal mortality. This issue became important in health research area especially human reproduction both in developed and developing country. In 2015 Indonesia rank fifth as the country with the highest number of premature babies in the world. The ability to reduce the number of preterm birth is to reduce risk factors associated with it. This research will be made the prediction model of preterm birth using hybrid multivariate adaptive regression splines (MARS) and Support Vector Machine (SVM). MARS used to select the attributes which suspected to affect premature babies. The result of this research is prediction model based on hybrid MARS-SVM obtains better performance than the other models

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