A two‐stage neural network prediction of chronic kidney disease
نویسندگان
چکیده
منابع مشابه
Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques
Background: Millions of deaths occur around the world each year due to lack of access to appropriate treatment for chronic kidney disease patients. Given the importance and mortality rate of this disease, early and low-cost prediction is very important. The researchers intend to identify chronic kidney disease through the optimal combination of techniques used in different stages of data mining...
متن کاملInternational Network of Chronic Kidney Disease cohort studies (iNET-CKD): a global network of chronic kidney disease cohorts
BACKGROUND Chronic kidney disease (CKD) is a global health burden, yet it is still underrepresented within public health agendas in many countries. Studies focusing on the natural history of CKD are challenging to design and conduct, because of the long time-course of disease progression, a wide variation in etiologies, and a large amount of clinical variability among individuals with CKD. With...
متن کاملEndocrine disorders in chronic kidney disease
Background and Objective: Endocrine disorders are common in patients with chronic kidney disease (CKD). The aim of the present study is reviewing available literature to give a deep understanding of complexities of endocrine disorders in chronic kidney disease. Methods: A narrative reviewing method based on the available literature was approached. Findings: Generally, when renal function de...
متن کاملA Reduced Set of Features for Chronic Kidney Disease Prediction
Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IET Systems Biology
سال: 2021
ISSN: 1751-8849,1751-8857
DOI: 10.1049/syb2.12031