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A model fusion approach for severity prediction of diabetes with respect to binary and multiclass classification

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Abstract

Diabetes Mellitus has impacted millions of people across the globe and continues with the same. It is caused due to increased blood sugar levels, as the pancreas fails to produce enough insulin and if produced, is not able to perform its function, thereby leading to excessive infiltration of sugars into the bloodstream. Seeing the ever-increasing risk associated with this disease, it is evident that a concrete solution for accurate timely prediction of diabetes for its necessary treatment is the need of the hour. In this paper, we have come up with a model for the timely prediction of diabetes mellitus as well as its severity level, using Machine Learning (ML) approaches. This work incorporates both binary as well as multiclass classification of diabetes, i.e., classifying patients into diabetic and non-diabetic, and further classifying diabetic patients into Type-1 and Type-2 diabetic patients. A hybrid model is developed using the technique of model fusion, by combining ANN, AdaBoost, and RF, in series with a Logistic Regression Classifier, and comparing this model with the conventional models, like Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), AdaBoost, etc. The overall accuracy obtained for the binary classification hybrid model is 97% for the first dataset and 79% for the validation dataset. Similarly, in the case of multiclass classification, the hybrid model gave an accuracy of 99% for the first dataset and 89% for the validation dataset. This clearly showed that the hybrid model performed better as compared to the existing conventional models in all aspects. Moreover, diabetic patients can also be classified as either having Type-1 or Type-2 diabetes. In addition to this, the severity level is predicted for the diabetic patients, as per a severity index table, formulated based on their respective glucose levels and age.

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Data availability

The study’s data was taken from a website and is freely accessible. We thank the authors and collaborators for making the original data freely available.

References

  1. Laakso Markku (1999) Hyperglycemia and cardiovascular disease in type 2 diabetes. Diabetes 48(5):937–942

    Article  CAS  PubMed  Google Scholar 

  2. Tiwari S, Agarwal S (2021) Data stream management for CPS-based healthcare: a contemporary review. IETE Tech Rev 39:987–1010

    Article  Google Scholar 

  3. Balaji R, Duraisamy R, Kumar MP (2019) Complications of diabetes mellitus: a review. Drug Invent Today 12(1)

  4. Tiwari Sadhana, Agarwal Sonali (2023) Empirical analysis of chronic disease dataset for multiclass classification using optimal feature selection based hybrid model with spark streaming. Future Gener Comput Syst 139:87–99

    Article  Google Scholar 

  5. Lovic Dragan et al (2020) The growing epidemic of diabetes mellitus. Curr Vasc Pharmacol 18(2):104–109

    Article  CAS  PubMed  Google Scholar 

  6. Kharroubi Akram T, Darwish Hisham M (2015) Diabetes mellitus: the epidemic of the century. World J Diabetes 6(6):850

    Article  PubMed  PubMed Central  Google Scholar 

  7. Atkinson Mark A, Eisenbarth George S, Michels Aaron W (2014) Type 1 diabetes. Lancet 383(9911):69–82

    Article  PubMed  Google Scholar 

  8. Annamalai R, Nedunchelian R (2023) Design of optimal bidirectional long short term memory based predictive analysis and severity estimation model for diabetes mellitus. Int J Inf Technol 15(1):447–455

    Google Scholar 

  9. Chatterjee Sudesna, Khunti Kamlesh, Davies Melanie J (2017) Type 2 diabetes. Lancet 389(10085):2239–2251

    Article  CAS  PubMed  Google Scholar 

  10. Patil Shashikant et al (2020) Performance overview of an artificial intelligence in biomedics: a systematic approach. Int J Inf Technol 12(3):963–973

    MathSciNet  Google Scholar 

  11. Mohapatra Debasis et al (2022) Distribution preserving train-test split directed ensemble classifier for heart disease prediction. Int J Inf Technol 14(4):1763–1769

    Google Scholar 

  12. Sisodia Deepti, Sisodia Dilip Singh (2018) Prediction of diabetes using classification algorithms. Procedia Comput Sci 132:1578–1585

    Article  Google Scholar 

  13. Tomar D, Agarwal S (2016) Multi-class twin support vector machine for pattern classification. In: Proceedings of 3rd international conference on advanced computing, networking and informatics: ICACNI 2015, vol 1. Springer. pp 97–110

  14. Ayon Safial Islam, Islam Md Milon (2019) Diabetes prediction: a deep learning approach. Int J Inf Eng Electron Bus 12(2):21

    Google Scholar 

  15. Naz Huma, Ahuja Sachin (2020) Deep learning approach for diabetes prediction using PIMA Indian dataset. J Diabetes Metab Disord 19:391–403

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hasan Md Kamrul et al (2020) Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access 8:76516–76531

    Article  Google Scholar 

  17. El Jerjawi NS, Abu-Naser SS (2018) Diabetes prediction using artificial neural network

  18. Patra R et al (2021) Analysis and prediction of Pima Indian Diabetes Dataset using SDKNN classifier technique. IOP Conf Ser Mater Sci Eng 1070(1):012059

    Article  Google Scholar 

  19. Patil V, Ingle DR (2021) Comparative analysis of different ML classification algorithms with diabetes prediction through Pima Indian diabetics dataset. In: 2021 International conference on intelligent technologies (CONIT). IEEE. pp 1–9

  20. Rahman SFA et al (2022) Multiclass classification scheme for diagnosis of diabetes mellitus based on type-1 fuzzy systems. In: AIP Conference Proceedings, vol 2472, no 1. AIP Publishing LLC. p 030001

  21. Diabetes dataset from the records of a Frankfurt Hospital. https://www.kaggle. com/datasets/johndasilva/diabetes. Accessed: June 5, 2022

  22. Diabetes Dataset from the National Institute of Diabetes and Digestive and Kidney Diseases. https://www.kaggle.com/datasets/mathchi/diabetes-data-set. Accessed 5 June 2022

  23. Chawla Nitesh V et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  Google Scholar 

  24. Fernández Alberto et al (2018) SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Artif Intell Res 61:863–905

    Article  MathSciNet  Google Scholar 

  25. Tomar Divya, Agarwal Sonali (2015) An effective weighted multi-class least squares twin support vector machine for imbalanced data classification. Int J Comput Intell Syst 8(4):761–778

    Article  Google Scholar 

  26. Gárate-Escamila Anna Karen, Hassani Amir Hajjam El, Andrès Emmanuel (2020) Classification models for heart disease prediction using feature selection and PCA. Inform Med Unlocked 19:100330

    Article  Google Scholar 

  27. Liashchynskyi P, Liashchynskyi P (2019) Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv preprint arXiv:1912.06059

  28. Sarwar Abid et al (2020) Diagnosis of diabetes type-II using hybrid machine learning based ensemble model. Int J Inf Technol 12:419–428

    ADS  Google Scholar 

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Authors and Affiliations

  1. Jamia Millia Islamia, New Delhi, India

    Mohammad Zohair

  2. Allahabad, Prayagraj, India

    Ritesh Chandra, Sadhana Tiwari & Sonali Agarwal

Authors
  1. Mohammad Zohair
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  2. Ritesh Chandra
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  3. Sadhana Tiwari
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  4. Sonali Agarwal
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Correspondence to Sadhana Tiwari.

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Zohair, M., Chandra, R., Tiwari, S. et al. A model fusion approach for severity prediction of diabetes with respect to binary and multiclass classification. Int. j. inf. tecnol. 16, 1955–1965 (2024). https://doi.org/10.1007/s41870-023-01463-9

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  • DOI: https://doi.org/10.1007/s41870-023-01463-9

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