Chronic Kidney Disease Survival Prediction with Artificial Neural Networks

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

doi:10.1109/bibm.2018.8621294

Cited by 40 publications

(21 citation statements)

References 11 publications

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“…AI research is witnessing widespread adoption in the prediction, detection, diagnosis, classification, treatment, and survival prediction of diseases [ 30 , 37 ]. The most common application of AI technologies is reflected in the domains of medical classification and quality of care.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence in Health Care: Bibliometric Analysis

Guo¹,

Hao²,

Zhao³

et al. 2020

J Med Internet Res

264

153

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Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

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Section: Discussionmentioning

confidence: 99%

“…The studies in these clusters focus on testing AI techniques and translating these techniques into practical settings [ 37 , 43 ]. The next step of health care–related AI research may transform from lab-based research to the development of clinically validated and safe regulated systems.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Health Care: Bibliometric Analysis

Guo¹,

Hao²,

Zhao³

et al. 2020

J Med Internet Res

264

153

View full text Add to dashboard Cite

show abstract

“…From the analysis it is concluded that radial basis function has performed well with 85.3% accuracy. Hanyu Zhang et al [13] explored neural network (NN) technique for predicting survivability of chronic kidney ailment patients. The dataset they have considered is taken from a hospital in Taiwan.…”

Section: Literature Surveymentioning

confidence: 99%

Diabetes Correlated Renal Fault Prediction through Deep Learning

Reddy¹,

Sethi²,

Rajender³

2020

EAI Endorsed Transactions on Pervasive Health and Technology

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INTRODUCTION: Diabetic nephropathy is one of the complications of diabetes that causes damage to kidneys. Deep learning techniques are widely used to predict different diseases. OBJECTIVES: The main aim of this work is to develop an effective prediction model using deep learning. To get an effective model, a suitable dataset is considered that comprises of features related to diabetic nephropathy. METHODS: Deep belief network (DBN) is the proposed deep learning technique which is compared with naive bayes, CART decision tree, logistic regression and support vector machine. DBN is composed of Restricted Boltzmann Machines (RBM). The algorithms are analysed based on evaluation measures like area under PR curve, area under ROC curve, gini coefficient and jaccard index. RESULTS: After comparison of all algorithms, it was observed that DBN has performed better in terms of AUROC, gini coefficient and jaccard index with values 0.8203, 0.6406 and 0.7777 respectively. But CART obtained better value of 0.9039 only for AUPR. CONCLUSION: The proposed technique has outperformed other techniques in terms of three metrics and is identified as the best performing algorithm. Hence, it is suggested to use DBN while predicting diabetic nephropathy.

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“…Some models have made it possible to detect kidney stones [19], [20] from the results of laboratory tests such as creatinine, uric acid, glucose, lymphocytes and other blood components. Other work has focused on predicting the survival of patients with CKD [21]. Neural networks and other machine learning techniques have also been applied to identify a patient's stage of chronic kidney disease [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

et al. 2019

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This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network's prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population. INDEX TERMS Chronic kidney disease prediction, neural networks, case-based reasoning, twin systems, explainable AI, support vector machines, random forest.

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Chronic Kidney Disease Survival Prediction with Artificial Neural Networks

Cited by 40 publications

References 11 publications

Artificial Intelligence in Health Care: Bibliometric Analysis

Artificial Intelligence in Health Care: Bibliometric Analysis

Diabetes Correlated Renal Fault Prediction through Deep Learning

Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

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