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Statistical and Predictive Analytics of Chronic Kidney Disease

  • Safae Sossi AlaouiEmail author
  • Brahim Aksasse
  • Yousef Farhaoui
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)

Abstract

Currently, health problems increasingly intrigue the curiosity of data scientists. In fact, data analytics as a rapidly evolving area can be the right solution to manage, detect and predict diseases which threaten human life and cause a high economic cost to health systems. This paper seeks to establish a statistical and predictive analysis of an available dataset related to chronic kidney disease (CKD) by employing the widely used software package called IBM SPSS. Indeed, we manage to create a 100% accurate model based on XGBoost linear machine learning algorithm for successful classification of patients into; affected by CKD or not affected.

Keywords

Chronic kidney disease Statistics Predictive analytics IBM SPSS Machine learning Classification 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Safae Sossi Alaoui
    • 1
    Email author
  • Brahim Aksasse
    • 1
  • Yousef Farhaoui
    • 1
  1. 1.Faculty of Sciences and Techniques, Department of Computer Science, M2I Laboratory, ASIA TeamMoulay Ismail UniversityErrachidiaMorocco

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