Skip to main content

Advertisement

Log in

Artificial Intelligence-Based Healthcare Data Analysis Using Multi-perceptron Neural Network (MPNN) Based On Optimal Feature Selection

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

In the healthcare industry, early identification of diabetes is a critical task to safeguard human life. Many individuals suffer the consequences of undetected diabetes due to the lack of consistent early identification of the disease's nature. The integration of Machine Learning (ML) and artificial intelligence models has emerged as a remarkable advancement in identifying diseases for prompt treatment. However, existing techniques have struggled to prioritize the importance of diabetic features, resulting in lower precision rates and poor prediction accuracy. This research proposes an improved diabetic disease prediction system based on Multi Perceptron Neural Network (MPNN). Initially, preprocessing is conducted to eliminate noise from the diabetic dataset. Subsequently, diabetic margin features are scrutinized to identify crucial features using a Social Spider Optimized Support Scalar Vector (SSOSSV). The selected features are then trained using the MPNN neural network to predict diabetic features. The proposed system efficiently identifies diabetic patients by analyzing feature sets with marginalized medical margins. By the identification, the data is securely processed with blockchain security by utilizing the AES algorithm, increasing round key increments can enhance system security and performance. The results demonstrate the effectiveness of the proposed diabetic prediction model, showcasing higher prediction rates, improved precision, recall rates, F1 measures, and reduced false rates, Higher security and authentication and verification which is all achieved with manageable time complexity. The proposed MPNN method has achieved significant improvement, increasing accuracy to 94.46%, and blockchain technology improves security for diabetic patient records.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The corresponding author can provide the dataset generated and analyzed during this study upon reasonable request.

References

  1. Niharika Patel, Manoranajan Panda. 2022. “The Framework of Privacy-Preserving Diabetes Prediction using Blockchain”, www.ijcrt.org © 2022 IJCRT. 10: 7 2320–2882.

  2. Firdous S, Wagai GA, Sharma K. A survey on diabetes risk prediction using machine learning approaches. J Fam Med Primary Care. 2022. https://doi.org/10.4103/jfmpc.jfmpc_502_22.

    Article  Google Scholar 

  3. Kakoly IJ, Hoque MR, Hasan N. Data-driven diabetes risk factor prediction using machine learning algorithms with feature selection technique. Sustainability. 2023;15:4930. https://doi.org/10.3390/su15064930.

    Article  Google Scholar 

  4. MitushiSoni, "Diabetes Prediction using Machine Learning Techniques", International Journal of Engineering Research & Technology (IJERT), http://www.ijert.org ISSN: 2278–0181 IJERTV9IS090496 (This work is licensed under a Creative Commons Attribution 4.0 International License.), Published by: www.ijert.org Vol. 9 Issue 09, September-2020.

  5. Watanabe M, Eguchi A, Sakurai K, et al. Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan environment and children’s study. Sci Rep. 2023;13:17419. https://doi.org/10.1038/s41598-023-44313-1.

    Article  Google Scholar 

  6. Ahmed U, et al. Prediction of diabetes empowered with fused machine learning. IEEE Access. 2022;10:8529–38. https://doi.org/10.1109/ACCESS.2022.3142097.

    Article  Google Scholar 

  7. Sivashankari R, Sudha M, Hasan MK. An empirical model to predict the diabetic positive using stacked ensemble approach. Front Public Health. 2022. https://doi.org/10.3389/fpubh.2021.792124.

    Article  Google Scholar 

  8. Perveen S, Shahbaz M, Saba T, Keshavjee K, Rehman A, Guergachi A. Handling irregularly sampled longitudinal data and prognostic modeling of diabetes using machine learning technique. IEEE Access. 2020;8:21875–85. https://doi.org/10.1109/ACCESS.2020.2968608.

    Article  Google Scholar 

  9. Wee BF, Sivakumar S, Lim KH, et al. Diabetes detection based on machine learning and deep learning approaches. Multimed Tools Appl. 2024;83:24153–85. https://doi.org/10.1007/s11042-023-16407-5.

    Article  Google Scholar 

  10. IsfafuzzamanTasin TU, Nabil SI, Khan R. Diabetes prediction using machine learning and explainable AI techniques. Healthcare Tech Letters. 2022. https://doi.org/10.1049/htl2.12039.

    Article  Google Scholar 

  11. Ismail L, Hennebelle A. Secure and privacy-preserving automated end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction. arXiv e-prints. 2022;29:2211.

    Google Scholar 

  12. Hennebelle A, Ismail L, Materwala H, Al Kaabi J, Ranjan P, Janardhanan R. Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction. Comput Struct Biotechnol J. 2023;23(23):212–33. https://doi.org/10.1016/j.csbj.2023.11.038.PMID:38169966;PMCID:PMC10758733[12].

    Article  Google Scholar 

  13. Khan FA, Zeb K, Al-Rakhami M, Derhab A, Bukhari SAC. Detection and prediction of diabetes using data mining: a comprehensive review. IEEE Access. 2021;9:43711–35. https://doi.org/10.1109/ACCESS.2021.3059343.

    Article  Google Scholar 

  14. Marzouk R, Alluhaidan AS, El Rahman SA. An analytical predictive models and secure web-based personalized diabetes monitoring system. IEEE Access. 2022. https://doi.org/10.1109/ACCESS.2022.3211264.

    Article  Google Scholar 

  15. Li J, Huang J, Zheng L, Li X. “Application of artificial intelligence in diabetes education and management”, present status and promising prospect. Front Public Health. 2020;8:173. https://doi.org/10.3389/fpubh.2020.00173.

    Article  Google Scholar 

  16. Fitriyani NL, Syafrudin M, Alfian G, Rhee J. Development of disease prediction model based on ensemble learning approach for diabetes and hypertension. ieee access. 2019;7:144777–89. https://doi.org/10.1109/access.2019.2945129.

    Article  Google Scholar 

  17. Annuzzi G, et al. Impact of nutritional factors in blood glucose prediction in type 1 diabetes through machine learning. IEEE Access. 2023;11:17104–15. https://doi.org/10.1109/ACCESS.2023.3244712.

    Article  Google Scholar 

  18. Hu P, et al. Prediction of new-onset diabetes after pancreatectomy with subspace clustering based multi-view feature selection. IEEE J Biomed Health Inform. 2023;27(3):1588–99. https://doi.org/10.1109/JBHI.2022.3233402.

    Article  Google Scholar 

  19. Neelakandan S, Rene Beulah J, Prathiba L, Murthy GLN, FantinIrudaya Raj E, Arulkumar N. Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model. Int J Model, Simulat Sci Comput. 2022;13(04):2241006.

    Article  Google Scholar 

  20. Mahiddin NB, Othman ZA, Bakar AA, Rahim NAA. An Interrelated decision-making model for an intelligent decision support system in healthcare. IEEE Access. 2022;10:31660–76. https://doi.org/10.1109/ACCESS.2022.3160725.

    Article  Google Scholar 

  21. Theis J, Galanter WL, Boyd AD, Darabi H. Improving the In-hospital mortality prediction of diabetes icu patients using a process mining/deep learning architecture. IEEE J Biomed Health Inform. 2022;26(1):388–99. https://doi.org/10.1109/JBHI.2021.3092969.

    Article  Google Scholar 

  22. Luque-Chang A, Cuevas E, Fausto F, Zaldívar D, Pérez M. Social spider optimization algorithm: modifications, applications, and perspectives. Mathemat Probl Eng. 2018. https://doi.org/10.1155/2018/6843923.

    Article  Google Scholar 

  23. Rabie O, Alghazzawi D, Asghar J, Saddozai FK, Asghar MZ. A Decision Support System for Diagnosing Diabetes Using Deep Neural Network. Front Public Health. 2022;17(10):861062. https://doi.org/10.3389/fpubh.2022.861062.PMID:35372240;PMCID:PMC8970706.

    Article  Google Scholar 

  24. Shynu PG, Menon VG, Kumar RL, Kadry S, Nam Y. Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing. IEEE Access. 2021;9:45706–20. https://doi.org/10.1109/ACCESS.2021.3065440.

    Article  Google Scholar 

  25. Thenappan S, ValanRajkumar M, Manoharan PS. Predicting diabetes mellitus using modified support vector machine with cloud security. IETE J Res. 2020. https://doi.org/10.1080/03772063.2020.1782781.

    Article  Google Scholar 

  26. Feng X, Cai Y, Xin R. Optimizing diabetes classification with a machine learning-based framework. BMC Bioinformatics. 2023;24:428. https://doi.org/10.1186/s12859-023-05467-x.

    Article  Google Scholar 

  27. Rashid H, Abdulazeez AM. Data mining classification techniques for diabetes prediction. Qubahan Acad J. 2021. https://doi.org/10.48161/qaj.v1n2a55.

    Article  Google Scholar 

  28. Jeba Sonia J. “Machine-learning-based diabetes mellitus risk prediction using multi-layer neural network no-prop algorithm.” Diagnostics. 2023;13(4):723. https://doi.org/10.3390/diagnostics13040723.

    Article  Google Scholar 

  29. Le TM, Vo TM, Pham TN, Dao SVT. A novel wrapper-based feature selection for early diabetes prediction enhanced with a metaheuristic. IEEE Access. 2021;9:7869–84. https://doi.org/10.1109/ACCESS.2020.3047942.

    Article  Google Scholar 

  30. Butt UM, Letchmunan S, Ali M, Hassan FadratulHafinaz, AneesBaqir HH, Sherazi R. Machine learning based diabetes classification and prediction for healthcare applications. J Healthc Eng. 2021. https://doi.org/10.1155/2021/9930985.

    Article  Google Scholar 

  31. Edeh MO, Khalaf OI. A classification algorithm-based hybrid diabetes prediction model. Front Public Health. 2022. https://doi.org/10.3389/fpubh.2022.829519.

    Article  Google Scholar 

  32. ShamreenAhamed B, Arya MS, Sangeetha SKB, Auxilia NV, Osvin. Diabetes mellitus disease prediction and type classification involving predictive modeling using machine learning techniques and classifiers. Appl Comput Intelligence Soft Comput. 2022. https://doi.org/10.1155/2022/7899364.

    Article  Google Scholar 

  33. Maniruzzaman Md, Jahanur Rahman Md, BenojirAhammed. Classification and prediction of diabetes disease using machine learning paradigm. Health Informat Sci Syst. 2020;8(1):1–14. https://doi.org/10.1007/s13755-019-0095-z.

    Article  Google Scholar 

  34. Li AUHJP, Khan J. Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data. Sensors. 2020;20(9):2649. https://doi.org/10.3390/s20092649.

    Article  Google Scholar 

  35. Kopitar L, Kocbek P, Cilar L, et al. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci Rep. 2020;10:11981. https://doi.org/10.1038/s41598-020-68771-z.

    Article  Google Scholar 

  36. Zhou H, Myrzashova R, Zheng R. Diabetes prediction model based on an enhanced deep neural network. J Wireless Com Network. 2020;2020:148. https://doi.org/10.1186/s13638-020-01765-7.

    Article  Google Scholar 

  37. Hasan MK, Alam MA, Das D, Hossain E, Hasan M. Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access. 2020;8:76516–31. https://doi.org/10.1109/ACCESS.2020.2989857.

    Article  Google Scholar 

  38. Saxena R, Sharma SK, Manali Gupta GC, Sampada. A novel approach for feature selection and classification of diabetes mellitus: machine learning methods. Comput Intell Neurosci. 2022. https://doi.org/10.1155/2022/3820360.

    Article  Google Scholar 

  39. Tasin I, Nabil TU, Islam S, Khan R. Diabetes prediction using machine learning and explainable AI techniques. HealthcTechnol Lett. 2022;10(1–2):1–10. https://doi.org/10.1049/htl2.12039.PMID:37077883;PMCID:PMC10107388.

    Article  Google Scholar 

  40. Kaul S, Kumar Y. Artificial intelligence-based learning techniques for diabetes prediction: challenges and systematic review. SN COMPUT SCI. 2020;1:322. https://doi.org/10.1007/s42979-020-00337-2.

    Article  Google Scholar 

  41. Alghamdi T. “Prediction of diabetes complications using computational intelligence techniques". Appl Sci. 2023;13(5):3030. https://doi.org/10.3390/app13053030.

    Article  Google Scholar 

  42. Bassam G, Rouai A, Ahmad R, Khan MA. Diabetes prediction empowered with multi-level data fusion and machine learning. Int J Adv Comput Sci Appl (IJACSA). 2023. https://doi.org/10.14569/IJACSA.2023.0141062.

    Article  Google Scholar 

  43. Zou Q, Kaiyang Qu, Luo Y. Predicting diabetes mellitus with machine learning techniques. Front Genet. 2018. https://doi.org/10.3389/fgene.2018.00515.

    Article  Google Scholar 

  44. Ahmed N, RayhanAhammed Md, Manowarul Islam Md, Uddin A, Arnisha Akhter Md, AlaminTalukder BK, Paul. Machine learning-based diabetes prediction and development of smart web applications. Int J Cognit Comput Eng. 2021. https://doi.org/10.1016/j.ijcce.2021.12.001.

    Article  Google Scholar 

  45. Alhalaseh R, Ghani DA, AL-Mashhadany, “The Effect of Feature Selection on Diabetes Prediction Using Machine Learning”,. IEEE symposium on computers and communications (ISCC). Year. 2023;2023:1–7. https://doi.org/10.1109/ISCC58397.2023.10218243.

    Article  Google Scholar 

  46. Lukmanto RB, Suharjito AriadiNugroho, Akbar H. Early Detection of Diabetes Mellitus using Feature Selection and Fuzzy Support Vector Machine. Procedia Computer Science. 2019. https://doi.org/10.1016/j.procs.2019.08.140.

    Article  Google Scholar 

  47. Aggarwal K. Comparison of feature selection techniques for improved diabetes prediction using random forest. Int J Mech Eng. 2023. https://doi.org/10.56452/6-3-675.

    Article  Google Scholar 

  48. El-Sofany H, El-Seoud SA, Karam OH, Abd YM, El-Latif IATF, Taj-Eddin. A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App. Int J Intell Syst. 2024. https://doi.org/10.1155/2024/6688934.

    Article  Google Scholar 

  49. Verma G, Verma H. A multilayer perceptron neural network model for predicting diabetes. Int J Grid Distributed Comput. 2020. https://doi.org/10.13140/RG.2.2.23203.89126.

    Article  Google Scholar 

  50. Madhubala T, Umagandhi R, Sathiamurthi P. Diabetes prediction using improved artificial neural network using multilayer perceptron. SSRG Int J Elect Electron Eng. 2022. https://doi.org/10.14445/23488379.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledged the Jamal Mohamed College (Autonomous), Tiruchirappalli,Tamilnadu, India for supporting the research work by providing the facilities.

Funding

No funding received for this research.

Author information

Authors and Affiliations

Authors

Contributions

This research work was made possible by the author.

Corresponding author

Correspondence to M. Wasim Raja.

Ethics declarations

Conflict of interest

No conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raja, M.W. Artificial Intelligence-Based Healthcare Data Analysis Using Multi-perceptron Neural Network (MPNN) Based On Optimal Feature Selection. SN COMPUT. SCI. 5, 1034 (2024). https://doi.org/10.1007/s42979-024-03323-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-03323-0

Keywords

Navigation