Skip to main content

Computational Intelligence Approaches for Prediction of Chronic Kidney Disease

  • Conference paper
  • First Online:
Advances in Distributed Computing and Machine Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 302))

Abstract

Over the past few decades, it has been observed that there is a growing interest in the area of intelligence systems, such as Machine Learning. Machine learning has been extensively used in order to support medical specialists and clinicians in the help of forecast and diagnosis of various diseases. The aim of this study is to compare the performance of six supervision-based Machine Learning techniques, which are used in the prediction and detection of the chronic kidney disease outbreak. Machine learning techniques are used to solve clinical problems and medical diagnosis’ which have recently been developed. Hence, it is essential to have a framework that can instantly recognize the prevalence of kidney disease in thousands of samples. This research uses the chronic kidney disease dataset that contains 400 Kidney patient’s data including 25 parameters. Moreover, we evaluated the performance of six supervision-based machine learning classification techniques, which are: KNN, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes and Logistics Regression. The performance of the supervised machine learning classification techniques was validated with sensitivity, specificity, f1 measure and accuracy. In this experiment, NB and RF outperformed, they were found to be at 100% accuracy, whereas the DT achieved 98% accuracy. Moreover, the KNN, SVM, LR classification techniques achieved 96% accuracy. Our findings showed that both the Random Forest and Naïve Bayes classification techniques outperformed as compared to other classification techniques used to predict kidney disease of the patients tested. In summary, our study has emphasized the research trends and scope in relation to Chronic Kidney Disease and as well as clinical research areas by machine learning techniques, which have had an effective impact in biomedical fields.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chronic Kidney Disease (CKD) Symptoms, Treatment, Causes & Prevention - American Kidney Fund (AKF). [Online]. http://www.kidneyfund.org/kidney-disease/chronic-kidney-disease-ckd/. Accessed 12 Dec. 2018

  2. Hopwood, V., Donnellan, C., Hopwood, V., Donnellan, C.: Current context: neurological rehabilitation and neurological physiotherapy. Acupunct. Neurol. Cond. 39–51 (2010)

    Google Scholar 

  3. Craver, L., et al.: Mineral metabolism parameters throughout chronic kidney disease stages 1–5–achievement of K/DOQI target ranges. Nephrol. Dial. Transplant. 22(4), 1171–1176 (2007)

    Article  Google Scholar 

  4. Di Noia, T. et al.: An end stage kidney disease predictor based on an artificial neural networks ensemble. Expert Syst. Appl. 40(11), 4438–4445 (2013)

    Google Scholar 

  5. Chronic Kidney Disease Basics | Chronic Kidney Disease Initiative | CDC. [Online], https://www.cdc.gov/kidneydisease/basics.html. Accessed 12 Dec. 2018

  6. Aljaaf, A.J. et al.: Early prediction of chronic kidney disease using machine learning supported by predictive analytics. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–9 (2018)

    Google Scholar 

  7. Dwivedi, A.K.: Analysis of computational intelligence techniques for diabetes mellitus prediction. Neural Comput. Appl. 30(12), 3837–3845 (2018)

    Article  Google Scholar 

  8. Dwivedi, A.K.: Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput. Appl. 29(10), 685–693 (2018)

    Article  Google Scholar 

  9. Mezzatesta, S., Torino, C., De Meo, P., Fiumara G., Vilasi, A.: A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Comput. Methods Programs Biomed. 177, 9–15 (2019)

    Google Scholar 

  10. Ahmed, M.R., Hasan Mahmud, S.M., Hossin, M.A., Jahan, H., Haider Noori, S. R.: A cloud based four-tier architecture for early detection of heart disease with machine learning algorithms. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC), pp. 1951–1955 (2018)

    Google Scholar 

  11. Mahmud, S.M.H., Hossin, M.A., Ahmed, M.R., Noori, S.R.H., Sarkar, M.N.I.: Machine learning based unified framework for diabetes prediction. In: Proceedings of the 2018 International Conference on Big Data Engineering and Technology—BDET 2018, pp. 46–50 (2018)

    Google Scholar 

  12. Heydari, M., Teimouri, M., Heshmati, Z., Alavinia, S.M.: Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. Int. J. Diabetes Dev. Ctries. 36(2), 167–173 (2016)

    Article  Google Scholar 

  13. Singh, P., Singh, S., Pandi-Jain, G.S.: Effective heart disease prediction system using data mining techniques. Int. J. Nanomed. 13, 121–124 (2018)

    Article  Google Scholar 

  14. Kukar, M., Kononenko, I., Grošelj, C., Kralj, K., Fettich, J.: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artif. Intell. Med. 16(1), 25–50 (1999)

    Article  Google Scholar 

  15. Ahamed, N.U. et al.: Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions. PLoS One 13(9): e0203839 (2018)

    Google Scholar 

  16. Ahamed, N.U., Kobsar, D., Benson, L.C., Clermont, C.A., Osis, S.T., Ferber, R.: Subject-specific and group-based running pattern classification using a single wearable sensor. J. Biomech. 84, 227–233 (2019)

    Article  Google Scholar 

  17. UCI Machine Learning Repository: Chronic_Kidney_Disease Data Set. [Online], https://archive.ics.uci.edu/ml/datasets/chronic_kidney_disease. Accessed 08 Dec. 2018

  18. Confusion Matrix. [Online], http://www2.cs.uregina.ca/~hamilton/courses/831/notes/confusion_matrix/confusion_matrix.html. Accessed 20 Dec. 2018

  19. Anantha Padmanaban, K.R., Parthiban, G.: Applying machine learning techniques for predicting the risk of chronic kidney disease. Indian J. Sci. Technol. 9, 29 (2016)

    Google Scholar 

  20. Jena, L., Kamila, N.K.: Distributed data mining classification algorithms for prediction of chronic-kidney-disease. Int. J. Emerg 4(11), 110–118 (2015)

    Google Scholar 

  21. Salekin, A., Stankovic, J.: Detection of chronic kidney disease and selecting important predictive attributes. In: IEEE International Conference on Healthcare Informatics (ICHI), pp. 262–270 (2016)

    Google Scholar 

  22. Abdelaziz, A., Elhoseny, M., Salama, A.S., Riad, A.M.: A machine learning model for improving healthcare services on cloud computing environment. Measurement 119, 117–128 (2018)

    Article  Google Scholar 

  23. Roy, J., Ali, M.A., Ahmed, M.R., Sundaraj, K.: Machine learning techniques for predicting surface EMG activities on upper limb muscle: a systematic review. In: International Conference on Cyber Security and Computer Science, pp. 330–339. Springer, Cham (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md. Razu Ahmed .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahmed, M., Ali, M., Ahmed, N., Bhuiyan, T. (2022). Computational Intelligence Approaches for Prediction of Chronic Kidney Disease. In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_29

Download citation

Publish with us

Policies and ethics