Advertisement

Identifying the Risk Factors for Diabetic Retinopathy Using Decision Tree

  • Preecy Poulose
  • S. Saritha
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

The role of data mining in healthcare industry is to improve the health systems and to use these data analytics to identify the inefficiencies and to improve care. Accuracy is important when it comes to patient care, and handling this huge amount of data improves the quality of the healthcare system. Diabetic retinopathy is a disease mainly occurring in patients with high sugar level in their blood. The situation occurs when sugar levels in blood are high, thus causing damage to blood vessels. The blood vessels swell and leak and gradually affect the eye vision. This is not detected in early stage of diabetics, even though it affects the eyesight from the beginning. Decision tree classification helps to detect the problem in the initial stage that helps to find the risk factors that cause this disease. Better treatment is provided to those infected patients. A detailed analysis of the result from the decision tree classifier is also presented in this work, and decisive factors for diabetic retinopathy are concluded herewith.

Keywords

Diabetic retinopathy Electronic health records Decision tree 

References

  1. 1.
    Zhang Y, Tsai C-W, Hassan MM, Alamri A (2017) Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J 11(1) CrossRefGoogle Scholar
  2. 2.
    Hamsagayathri P, Sampath P (2017) Priority based decision tree classifier for breast cancer. In: 2017 international conference on advanced computing and communication systems (ICACCS-2017), 06–07 Jan 2017, Cancer DetectionGoogle Scholar
  3. 3.
    Senthilnayaki B, Venkatalakshmi K, Kannan A (2013) An intelligent intrusion detection system using genetic based feature selection and modified J48 decision tree classifier. In: 2013 fifth international conference on advanced computing (ICoAC)Google Scholar
  4. 4.
    Sarwinda D, Bustamam A (2017) A complete modelling of local binary pattern for detection of diabetic retinopathy. In: 2017 1st international conference on informatics and computational sciences (ICICoS) Google Scholar
  5. 5.
    Dhanasekaran R, Mahendran G, Murugeswari S, Fargana SM (2016) Investigation of diabetic retinopathy using GMM classifier. In: 2016 international conference on advanced communication control and computing technologies (ICACCCT)Google Scholar
  6. 6.
    Mahendran G, Dhanasekaran R (2014) Identification of exudates for diabetic retinopathy based on morphological process and PNN classifier. In: International conference on communication and signal processing, 3–5 Apr 2014Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Rajagiri School of Engineering and TechnologyKochiIndia

Personalised recommendations