Open Access Journal

ISSN : 2456-1304 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Science Engineering and Management (IJSEM)

Monthly Journal for Science Engineering and Management

ISSN : 2456-1304 (Online)

Analysis and prediction of Chronic Kidney Disease using Machine Learning Algorithms

Author : Srinitya G 1 Daniel Madan Raja S 2

Date of Publication :24th October 2019

Abstract: Health is Wealth: Today the world has taken a step forward where each individual is concerned about what he is consuming on a daily basis and analyses the after effects of the food. Every individual is more concerned about his/her everyday food habits and tries to adapt himself to what nature provides him. We are moving towards a technology oriented living where computers in general and data science and analysis in particular plays a major role in every field. A recent survey from World Health Organization (WHO) tells us that the growth of ageing population may increase by 50% in the forth coming decade. Here, in this paper we mainly concentrate on kidney related issues, and try to predict the presence of chronic kidney disease based on certain parameters available from UCI dataset using decision tree based approach

Reference :

    1.  https://www.who.int/ageing/10-priorities/en/ accessed on 21 Feb 2019.
    2.  https://www.who.int/ageing/en/ accessed on 21 Feb 2019.
    3.  Ling Yu, Duke Billie J. et al. Exosomal Gapdh from proximal tubule cells regulate ENaC activity Nov 2016 4. Verhaar MC, knepper MA et al. Exosomes and the kidney: prospects for diagnosis and therapy of renal diseases. Kidney Int. 80:1138-1145 Aug 2011.
    4. Mirja k, Samoylenko A et al. Exosomes as renal inductive signals in health and disease, and their application as diagnostic markers and therapeutic agents. Front. Cell Dev. Biol. 2015 Oct.
    5. GuillermoGarcia-Garcia, kunitoshi Iseki et al. Chronic kidney disease: global dimension and perspectives. May 2013.
    6.  Tharmarajah Thiruvaran et al., IEEE Identifying important attributes for early detection of chronic kidney disease.
    7. Shuo Yang, Ran Wei et al. Semantic inference on clinical documents: Combining Machine learning algorithms with an inference engine for effective clinical diagnosis and treatment (2017)
    8. Meenambal S. et al. Velocity bounded Boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis. Expert Syst. Appl. 28-47 (2016).
    9. J Stankovic, Salekin A Detection of chronic kidney disease and selecting important predictive attributes in healthcare informatics (ICHI), IEEE, pp.262-270, oct 2016. 

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