BibTex Citation Data :
@article{Medstat10086, author = {Moch. Mukid and Triastuti Wuryandari and Desy Ratnaningrum and Restu Sri Rahayu}, title = {BAGGING CLASSIFICATION TREES UNTUK PREDIKSI RISIKO PREEKLAMPSIA (Studi Kasus : Ibu Hamil Kategori Penerima Jampersal di RSUD Dr. Moewardi Surakarta)}, journal = {MEDIA STATISTIKA}, volume = {8}, number = {2}, year = {2015}, keywords = {}, abstract = { Preeclampsia is a spesific pregnancy disease in which hypertency and proteinuria occurs after 20 weeks of pregnancy. Classification Trees is a statistical method that can be used to identify potency of expectant women suffering from preeclampsia. This research aim to predict the risk of preeclampsia based on some individual variables. They are parity, work status, history of hypertension of preeclampsia, body mass index, education and income. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification Trees method. By the method, classification accuracy reach to 86%. Keywords : Pre-eclampsia, Bagging CART, Classification Accuracy }, issn = {2477-0647}, pages = {111--120} doi = {10.14710/medstat.8.2.111-120}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/10086} }
Refworks Citation Data :
Preeclampsia is a spesific pregnancy disease in which hypertency and proteinuria occurs after 20 weeks of pregnancy. Classification Trees is a statistical method that can be used to identify potency of expectant women suffering from preeclampsia. This research aim to predict the risk of preeclampsia based on some individual variables. They are parity, work status, history of hypertension of preeclampsia, body mass index, education and income. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification Trees method. By the method, classification accuracy reach to 86%.
Keywords : Pre-eclampsia, Bagging CART, Classification Accuracy
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