Skip to main content
SpringerLink
Log in

Study and Impact Analysis of Machine Learning Approaches for Smart Healthcare in Predicting Mellitus Diabetes on Clinical Data

  • Chapter
  • First Online:
Smart Healthcare Analytics: State of the Art

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 213))

Abstract

Diabetes is a chronic disease that results in too much glucose in the blood. In current scenario it becomes a communal health crisis in human beings. Sometimes it causes saviour injuries to mammals leading them to death. To evade loss of precious lives several machine learning methodologies are implemented for the prediction of diabetes. The task of predictive analytics is very challenging but it can eventually assist the physicians to predict the disease and notify them about the victim’s condition. The victim could be cured by early treatment or by taking precautions before the patient is being attacked. In this research work six different types of supervised machine learning (ML) algorithms are used to solve this hazardous dilemma such as “Support Vector Machine (SVM)”, “Decision Tree (DT)”, “Artificial Neural Network (ANN)”, “Random Forest (RF)”, “K-Nearest Neighbor (KNN)” and “Logistic Regression (LR)” are applied on the “Pima Indians (PIDD) dataset”. On the basis of accuracy precision the effectiveness of the classifier is evaluated. For experimental purpose, the details of a patient’s medical record are applied on the dataset. By discerning this medical record a scholar can inspect the symptoms and can effortlessly predict the stage to which the patient belongs. By this early extrapolation the patient can acquire some recommended medication related to the life risky syndrome to ensure the safety of the life of the patient shortly. Smart healthcare includes smartly handle the patient from past data analysis for predicting chronic diabetes disease.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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. Kaur, D.R.: Diabetes self-management education programs: current scenario and relevance in India. Epidemiol. Int. 2(2), 4–8 (2017)

    Article  Google Scholar 

  2. Prema, N.S., Varshith, V., Yogeswar, J.: Prediction of diabetes using ensemble techniques. Int. J. Recent Technol. Eng. 7(6), 203–205 (2019)

    Google Scholar 

  3. Aishwarya Jakka, V.R.J.: Performance evaluation of machine learning models for diabetes prediction. Int. J. Innov. Technol. Explor. Eng. 8(11) (2019)

    Google Scholar 

  4. Patil, M.K.: Designing a model to detect diabetes using machine learning. Int. J. Eng. Res. Technol. 8(11), 333–340 (2019)

    Google Scholar 

  5. Nimmagadda, S.P., Yeruva, S., Siempu, R.: Improved diabetes prediction model for predicting type-II diabetes. Int. J. Innov. Technol. Explor. Eng. 8(12), 230–235 (2019). https://doi.org/10.35940/ijitee.L3594.1081219

    Article  Google Scholar 

  6. Zehra, A., Asmawaty, T., Aznan, M.A.M.: A Comparative Study on the Pre-processing and Mining of Pima Indian Diabetes Dataset. Univ. Malaysia Pahang, Kuantan, Pahang (2013)

    Google Scholar 

  7. Komi, M., Li, J., Zhai, Y., Zhang, X.: Application of data mining methods in diabetes prediction. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, pp. 1006–1010 (2017). https://doi.org/10.1109/ICIVC.2017.7984706

  8. Kandhasamy, J.P., Balamurali, S.: Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput. Sci. 47, 45–51 (2015). https://doi.org/10.1016/j.procs.2015.03.182

    Article  Google Scholar 

  9. Tafa, Z., Pervetica, N., Karahoda, B.: An intelligent system for diabetes prediction. In: 2015 4th Mediterranean Conference on Embedded Computing (MECO), Budva, pp. 378–382 (2015). https://doi.org/10.1109/MECO.2015.7181948

  10. Sisodia, D., Sisodia, D.S.: Prediction of diabetes using classification algorithms. Procedia Comput. Sci. 132(Iccids), 1578–1585 (2018). https://doi.org/10.1016/j.procs.2018.05.122

  11. Anand, A., Shakti, D.: Prediction of diabetes based on personal lifestyle indicators. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 673–676 (2015). https://doi.org/10.1109/NGCT.2015.7375206

  12. Santhanam, T., Padmavathi, M.S.: Application of K-Means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis. Procedia Comput. Sci. 47(C), 76–83 (2015). https://doi.org/10.1016/j.procs.2015.03.185

  13. Vijayan, V.V., Anjali, C.: Prediction and diagnosis of diabetes mellitus—a machine learning approach. In: 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015, no. December, pp. 122–127 (2016). https://doi.org/10.1109/RAICS.2015.7488400

  14. Wang, K.J., Adrian, A.M., Chen, K.H., Wang, K.M.: An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus. J. Biomed. Inform. 54, 220–229 (2015). https://doi.org/10.1016/j.jbi.2015.02.001

    Article  Google Scholar 

  15. Al Jarullah, A.A.: Decision tree discovery for the diagnosis of type II diabetes. In: International Conference on Innovations in Information Technology, pp. 303–307 (2011). https://doi.org/10.1109/INNOVATIONS.2011.5893838

  16. Sonar, P., Jayamalini, K.: Diabetes prediction using different. In: 2019 3rd International Conference on Computing Methodologies and Communication, no. Iccmc, pp. 367–371 (2019)

    Google Scholar 

  17. Dey, S.K.: Implementation of a web application to predict diabetes disease: an approach using machine learning algorithm. In: 2018 21st International Conference on Computer and Information Technology, pp. 1–5 (2018)

    Google Scholar 

  18. Sarwar, M.A., Kamal, N., Hamid, W., Shah, M.A.: Prediction of diabetes using machine learning algorithms in healthcare. In: ICAC 2018—2018 24th IEEE International Conference on Automation and Computing: Improving Productivity Through Automation and Computing, no. September, pp. 1–6 (2018). https://doi.org/10.23919/IConAC.2018.8748992

  19. Song, Y., Liang, J., Lu, J., Zhao, X.: An efficient instance selection algorithm for k nearest neighbor regression. Neurocomputing (2017). https://doi.org/10.1016/j.neucom.2017.04.018

    Article  Google Scholar 

  20. Karatsiolis, S., Schizas, C.N.: Region based support vector machine algorithm for medical diagnosis on Pima Indian Diabetes dataset. In: 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), pp. 139–144 (2012). https://doi.org/10.1109/BIBE.2012.6399663

  21. Kalaiselvi, C., Nasira, G.M.: A new approach for diagnosis of diabetes and prediction of cancer using ANFIS. In: 2014 World Congress on Computing and Communication Technologies, Trichirappalli, pp. 188–190 (2014). https://doi.org/10.1109/WCCCT.2014.66

  22. Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., Tang, H.: Predicting diabetes mellitus with machine learning techniques. 9(November), 1–10 (2018). https://doi.org/10.3389/fgene.2018.00515

  23. Thangarasu, G., Dominic, P.D.D.: Prediction of hidden knowledge from Clinical Database using data mining techniques. In: 2014 International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, pp. 1–5 (2014). https://doi.org/10.1109/ICCOINS.2014.6868414

  24. Nai-Arun, N., Moungmai, R.: Comparison of classifiers for the risk of diabetes prediction. Procedia Comput. Sci. 69, 132–142 (2015). https://doi.org/10.1016/j.procs.2015.10.014

    Article  Google Scholar 

  25. Yahyaoui, A., Jamil, A., Rasheed, J., Yesiltepe, M.: A decision support system for diabetes prediction using machine learning and deep learning techniques. In: 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey, pp. 1–4 (2019). https://doi.org/10.1109/UBMYK48245.2019.8965556

  26. Nilashi, M., Ibrahim, O., Dalvi, M., Ahmadi, H., Shahmoradi, L.: Accuracy improvement for diabetes disease classification: a case on a public medical dataset. Fuzzy Inf. Eng. 9(3), 345–357 (2017). https://doi.org/10.1016/j.fiae.2017.09.006

    Article  Google Scholar 

  27. Jain, D., Singh, V.: Feature selection and classification systems for chronic disease prediction: a review. Egypt. Inform. J. 19(3), 179–189 (2018). https://doi.org/10.1016/j.eij.2018.03.002

    Article  Google Scholar 

  28. Swapna, G., Kp, S., Vinayakumar, R.: ScienceDirect automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. Procedia Comput. Sci. 132(Iccids), 1253–1262 (2018). https://doi.org/10.1016/j.procs.2018.05.041

  29. Lekha, S.: Real-time non-invasive detection and classification of diabetes using modified convolution neural network. 2194(c), 1–7 (2017). https://doi.org/10.1109/JBHI.2017.2757510

  30. Joshi, T.N., Chawan, P.P.M.: Diabetes prediction using machine learning techniques. Ijera 8(1), 9–13 (2018). https://doi.org/10.9790/9622-0801020913

    Article  Google Scholar 

  31. Choudhury, A., Gupta, D.: A survey on medical diagnosis of diabetes using machine learning techniques. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds.) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol. 740. Springer, Singapore (2019)

    Google Scholar 

  32. Larabi-Marie-Sainte, S., Aburahmah, L., Almohaini, R., Saba, T.: Current techniques for diabetes prediction: review and case study. Appl. Sci. 9(21) (2019). https://doi.org/10.3390/app9214604

  33. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  34. Friedman, J.H., Baskett, F., Shustek, L.J.: An algorithm for finding nearest neighbors. IEEE Trans. Comput. C-24(10), 1000–1006 (1975). https://doi.org/10.1109/T-C.1975.224110

  35. Hosmer, D., Lemeshow, S.: Stepwise logistic regression. Applied logistic regression. In: Chapter 4, Model-Building Strategies and Methods for Logistic Regression, pp. 116–121 (2000)

    Google Scholar 

  36. Dagliati, A., et al.: Machine learning methods to predict diabetes complications. J. Diabetes Sci. Technol. 12(2), 295–302 (2018). https://doi.org/10.1177/1932296817706375

    Article  Google Scholar 

  37. Yadav, B., Sharma, S., Kalra, A.: Supervised learning technique for prediction of diseases. Adv. Intell. Syst. Comput. 624, 357–369 (2018). https://doi.org/10.1007/978-981-10-5903-2_37

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India

    Aisworya Mohanty, Bibudhendu Pati & Chhabi Rani Panigrahi

  2. Department of Computer Science and Engineering, GITA Autonomous College, BPUT, Bhubaneswar, India

    Sasmita Parida & Suvendu Chandan Nayak

Authors
  1. Aisworya Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sasmita Parida
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suvendu Chandan Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bibudhendu Pati
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chhabi Rani Panigrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

  2. Royal Adelaide Hospital, Adelaide, SA, Australia

    Ashlesha Vaidya

  3. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Suneeta Mohanty

  4. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Satarupa Mohanty

  5. School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW, Australia

    Ana Hol

Rights and permissions

Reprints and permissions

Copyright information

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

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mohanty, A., Parida, S., Nayak, S.C., Pati, B., Panigrahi, C.R. (2022). Study and Impact Analysis of Machine Learning Approaches for Smart Healthcare in Predicting Mellitus Diabetes on Clinical Data. In: Pattnaik, P.K., Vaidya, A., Mohanty, S., Mohanty, S., Hol, A. (eds) Smart Healthcare Analytics: State of the Art. Intelligent Systems Reference Library, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-5304-9_7

Download citation

Publish with us

Policies and ethics

Search

Navigation