Chronic Kidney Disease Survival Prediction with Artificial Neural Networks

Hanyu Zhang, Che Lun Hung, William Cheng Chung Chu, Ping Fang Chiu, Chuan Yi Tang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


The main objective of this work is to investigate the performance of Artificial Neural Network (ANN) models while applying to the survivability prediction on Chronic Kidney Disease (CKD) patients. Chronic kidney disease patients suffer from losing the primary function of the kidney, i.e., blood filtration, gradually over time. At the end stage, regular hemodialysis or kidney transplant are required to survive. During the process of disease development, patients may also suffer from complications of acidosis, anemia, diabetes, high blood pressure or neuropathy, etc., which in turn affects patients' quotidian life. It is reported that the median survival time of late-stage patients is only about 3 years. Evaluating precisely the condition of patients is of great importance as it would greatly help to decide appropriate care, medications or medical interventions needed, which among them have a complex interrelationship and influence the outcome of the individual patient. An accurate prediction model would hopefully be able to fit into that role and may be used to revise current treatment. However, due to the complex nature of the problem, as multiple interrelated factors may influence the patient's survival, finding such a model is a challenging task. Recently, artificial intelligence (AI), especially the deep learning technique has become a thriving field, owing to the rise of computational capability. As the approach needs no human specialist in specifying explicit knowledge in advance, but gathers knowledge automatically from amounts of data by building a hierarchy of concepts with complicated ones upon simpler ones, it has already been applied successfully in intuitive problems, like understanding speeches or images, making diagnoses in medicine or self-driving cars, etc. And conversely, technological advancements have been made available through practical applications, profiting from which we may develop prediction models for chronic kidney disease survivability. In this research, data preprocessing, data transformations, and artificial neural networks are used to establish the mapping from many clinical factors to the patient's survival. The computational results are also reported in the paper.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538654880
StatePublished - 21 Jan 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018


Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018


  • Artificial neural networks
  • chronic kidney disease
  • classification
  • deep learning
  • disease prediction


Dive into the research topics of 'Chronic Kidney Disease Survival Prediction with Artificial Neural Networks'. Together they form a unique fingerprint.

Cite this