Hybrid Support Vector Machine to Preterm Birth Prediction - Noviyanti Santoso; Sri Pingit Wulandari (IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 2018)
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