An end stage kidney disease predictor based on an artificial neural networks ensemble

Authors:
Tommaso Di Noia;Vito Claudio Ostuni;Francesco Pesce;Giulio Binetti;David Naso;Francesco Paolo Schena;Eugenio Di Sciascio
Affiliations:
Department of Electrical and Electronics Engineering, Politecnico of Bari, Italy;Department of Electrical and Electronics Engineering, Politecnico of Bari, Italy;Department of Emergency and Organ Transplantation, Nephrology Dialysis and Transplantation Unit, University of Bari, Bari, Italy and Clinical Cardiology and Imaging, Royal Brompton Campus, Nationa ...;Department of Electrical and Electronics Engineering, Politecnico of Bari, Italy and Automation and Robotics Research Institute, University of Texas at Arlington, Arlington, TX, USA;Department of Electrical and Electronics Engineering, Politecnico of Bari, Italy;Department of Emergency and Organ Transplantation, Nephrology Dialysis and Transplantation Unit, University of Bari, Bari, Italy and C.A.R.S.O. Consortium, Strada Prov. le Valenzano-Casamassima, I ...;Department of Electrical and Electronics Engineering, Politecnico of Bari, Italy
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

IgA Nephropathy (IgAN) is a worldwide disease that affects kidneys in human beings and leads to end-stage kidney disease (ESKD) thus requiring renal replacement therapy with dialysis or kidney transplantation. The need for new tools able to help clinicians in predicting ESKD risk for IgAN patients is highly recognized in the medical field. In this paper we present a software tool that exploits the power of artificial neural networks to classify patients' health status potentially leading to ESKD. The classifier leverages the results returned by an ensemble of 10 networks trained by using data collected in a period of 38years at University of Bari. The developed tool has been made available both as an online Web application and as an Android mobile app. Noteworthy to its clinical usefulness is that its development is based on the largest available cohort worldwide.