Prediction of Chronic Kidney Diseases Using Deep Artificial Neural Network Technique

  • Himanshu KriplaniEmail author
  • Bhumi Patel
  • Sudipta Roy
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 31)


The progression of the chronic kidney disease and methodologies to diagnose chronic kidney disease is a challenging problem which can reduce the cost of treatment. We studied 224 records of chronic kidney disease available on the UCI machine learning repository named chronic kidney diseases dating back to 2015. Our proposed method is based on deep neural network which predicts the presence or absence of chronic kidney disease with an accuracy of 97%. Compared to other available algorithms, the model we built shows better results which is implemented using the cross-validation technique to keep the model safe from overfitting. This automatic chronic kidney disease treatment helps reduce the kidney damage progression, but for this chronic kidney disease detection at initial stage is necessary.


Chronic kidney diseases Deep neural network Random forest Support vector machine Gradient descent Optimization 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUV Patel College of Engineering, Ganpat UniversityMehsanaIndia
  2. 2.Department of Computer Science and EngineeringInstitute of Computer Technology, Ganpat UniversityMehsanaIndia
  3. 3.Department of Computer Science & EngineeringCalcutta University Technology CampusKolkataIndia

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