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)


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.


Chronic kidney disease Statistics Predictive analytics IBM SPSS Machine learning Classification 


  1. 1.
  2. 2.
    Romagnani, P., Remuzzi, G., Glassock, R., Levin, A., Jager, K.J., Tonelli, M., Massy, Z., Wanner, C., Anders, H.-J.: Chronic kidney disease. Nat. Rev. Dis. Primer. 3, 17088 (2017)CrossRefGoogle Scholar
  3. 3.
    Levey, A.S., Coresh, J.: Chronic kidney disease. Lancet 379, 165–180 (2012)CrossRefGoogle Scholar
  4. 4.
    Pontillo, C., Zhang, Z.-Y., Schanstra, J.P., Jacobs, L., Zürbig, P., Thijs, L., Ramírez-Torres, A., Heerspink, H.J.L., Lindhardt, M., Klein, R., Orchard, T., Porta, M., Bilous, R.W., Charturvedi, N., Rossing, P., Vlahou, A., Schepers, E., Glorieux, G., Mullen, W., Delles, C., Verhamme, P., Vanholder, R., Staessen, J.A., Mischak, H., Jankowski, J.: Prediction of chronic kidney disease stage 3 by CKD273, a urinary proteomic biomarker. Kidney Int. Rep. 2, 1066–1075 (2017)CrossRefGoogle Scholar
  5. 5.
    Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access. 5, 8869–8879 (2017)CrossRefGoogle Scholar
  6. 6.
    Gunarathne, W., Perera, K.D.M., Kahandawaarachchi, K.: Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for Chronic Kidney Disease (CKD). In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 291–296. IEEE (2017)Google Scholar
  7. 7.
    Ghasemaghaei, M., Ebrahimi, S., Hassanein, K.: Data analytics competency for improving firm decision making performance. J. Strateg. Inf. Syst. 27, 101–113 (2018)CrossRefGoogle Scholar
  8. 8.
    Kestin, I.: Statistics in medicine (2018)CrossRefGoogle Scholar
  9. 9.
    Ryu, S.: Book Review: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die. Healthc. Inform. Res. 19, 63 (2013)CrossRefGoogle Scholar
  10. 10.
    Mikut, R., Reischl, M.: Data mining tools: data mining tools. Rev. Data Min. Knowl. Discov. 1, 431–443 (2011)CrossRefGoogle Scholar
  11. 11.
    UCI Machine Learning Repository: Chronic_Kidney_Disease Data Set.
  12. 12.
    Sossi Alaoui, S., Farhaoui, Y., Aksasse, B.: A comparative study of the four well-known classification algorithms in data mining. In: Advanced Information Technology, Services and Systems, pp. 362–373. Springer, Cham (2017)Google Scholar
  13. 13.
    Zhu, W., Zeng, N., Wang, N.: Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. In: NESUG Proceedings Health Care and Life Sciences, Baltimore, Maryland, vol. 19, p. 67 (2010)Google Scholar
  14. 14.
    Alberg, A.J., Park, J.W., Hager, B.W., Brock, M.V., Diener-West, M.: The use of “overall accuracy” to evaluate the validity of screening or diagnostic tests. J. Gen. Intern. Med. 19, 460–465 (2004)CrossRefGoogle Scholar
  15. 15.
    Wolpert, D.H.: The supervised learning no-free-lunch theorems. In: Soft Computing and Industry, pp. 25–42. Springer (2002)Google Scholar
  16. 16.
    Sossi Alaoui, S., Farhaoui, Y., Aksasse, B.: Classification algorithms in data mining. Int. J. Tomogr. SimulationTM 31, 34–44 (2018)Google Scholar

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

Personalised recommendations