An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients

BMC Bioinformatics

Proceedings

Guan-Mau Huang¹  Kai-Yao Huang¹  Julia Tzu-Ya Weng²  Tzong-Yi Lee² 

[1] Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan;Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan;Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taiwan;

关键词: Support Vector Machine;    Diabetic Nephropathy;    Random Forest;    Information Gain;    ADIPOQ Gene;   

DOI  :  10.1186/1471-2105-16-S1-S5

来源: Springer

【 摘 要 】

BackgroundThe prevalence of type 2 diabetes is increasing at an alarming rate. Various complications are associated with type 2 diabetes, with diabetic nephropathy being the leading cause of renal failure among diabetics. Often, when patients are diagnosed with diabetic nephropathy, their renal functions have already been significantly damaged. Therefore, a risk prediction tool may be beneficial for the implementation of early treatment and prevention.ResultsIn the present study, we developed a decision tree-based model integrating genetic and clinical features in a gender-specific classification for the identification of diabetic nephropathy among type 2 diabetic patients. Clinical and genotyping data were obtained from a previous genetic association study involving 345 type 2 diabetic patients (185 with diabetic nephropathy and 160 without diabetic nephropathy). Using a five-fold cross-validation approach, the performance of using clinical or genetic features alone in various classifiers (decision tree, random forest, Naïve Bayes, and support vector machine) was compared with that of utilizing a combination of attributes. The inclusion of genetic features and the implementation of an additional gender-based rule yielded better classification results.ConclusionsThe current model supports the notion that genes and gender are contributing factors of diabetic nephropathy. Further refinement of the proposed approach has the potential to facilitate the early identification of diabetic nephropathy and the development of more efficient treatment in a clinical setting.

【 授权许可】

Unknown   
© Huang et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.