Cheng-Hong Yang, Dony Novaliendry, Jin-Bor Chen, Fegie Y. Wattimena, Axelon S. Renyaan, Yaslinda Lizar, Asriwan Guci, Muhammad Ariyon, Dochi Ramadhani, Unung Verawardina, Irwan, Yenny Desnelita, Wilda Susanti, Gustientiedina, Hastuti Marlina, Arden Simeru, Torkis Nasution
Hemodialysis is one of modality to treat end stage kidney disease. This study is aimed to predict the mortality risk of hemodialysis patients. A total of 665 prevalent hemodialysis patients were enrolled in one hemodialysis center in Taiwan. The prediction is based on Support Vector Machine (SVM) which developed under MATLAB. Based on the obtained results, SVM performs better accuracy compared to K-Nearest Neighbor, logistic regression, a lineardiscriminant, Treeand ensemble. In addition, the F1-score of SVM is higher than that from other methods. The highest mortality risk factor is diabetes; the second is cardiovascular diseaseand small influence of related medical variables such as parathyroid surgery, urea reduction ratio, etc. © 2020, Fundacion Aigle.
Department of Electronic Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung, Taiwan; Department of Electronic Engineering, Faculty of Engineering, UniversitasNegeri Padang, Padang, Indonesia; Division of Nephrology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan; Fakultas Sains and Teknologi, UniversitasOttowGeissler Papua, Papua, Indonesia; UIN Imam Bonjol Padang, Padang, Indonesia; STIKesMercubaktijaya, Padang, Indonesia; Teknik Perminyakan, Universitas Islam Riau, Pekanbaru, Indonesia; Pendidikan TIK, IKIP PGRI Pontianak, Pontianak, Indonesia; Departement Computer Science, Institut Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia; Public Health, STIKes Hang Tuah, Pekanbaru, Indonesia; Sistem Informasi, Sekolah Tinggi Teknologi Pekanbaru, Pekanbaru, Indonesia; Computer Science, STMIK Amik Riau, Pekanbaru, Indonesia
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