Design of a Low-Complexity Deep Learning Model for Diagnosis of Type 2 Diabetes

References

1. Roglic G. Global Report on Diabetes. 2016 Available from:https://www.who.int/publications/i/item/9789241565257(accessed on 6-8-2024)
2. Murphy Z. Diabetes: Asia's 'silent killer'. 2013 Available from:https://www.bbc.com/news/world-asia-24740288#:~:text=Asia%20is%20in%20the%20grip,as%20it%20is%20by%20excess(accessed on 6-8-2024)
3. Allam F. Nossai Z. Gomma H. Ibrahim I. Abdelsalam M. A recurrent neural network approach for predicting glucose concentration in type-1 diabetic patients. Engineering Applications of Neural Networks. Berlin, Heidelberg Springer 2011 254 259 10.1007/978‑3‑642‑23957‑1_29
   [Google Scholar]
4. Nasser AR. Hasan AM. Humaidi AJ. lot and cloud computing in health-care: A new wearable device and cloud-based deep learning algorithm for monitoring of diabetes. Electronics (Basel) 2011 10 21 2719 10.3390/electronics10212719
   [Google Scholar]
5. Yamashita R. Nishio M. Do RKG Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018 9 4 611 29 10.1007/s13244‑018‑0639‑9 29934920
   [Google Scholar]
6. Swapna G. Vinayakumar R. Soman KP. Diabetes detection using deep learning algorithms. ICT express 2018 4 4 243 6
   [Google Scholar]
7. Nieminen J. Gomez C Isomaki M. Networking solutions for connecting bluetooth low energy enabled machines to the internet of things. IEEE Netw 2014 28 6 83 90 10.1109/MNET.2014.6963809
   [Google Scholar]
8. Pima Indians Diabetes Database. 2016 Available from: https://www.kaggle.com/datasets/uciml/pima-indians-diabetesdatabase (accessed on 6-8-2024)
   [Google Scholar]
9. Dey SK. Hossain A. Rahman MM. Implementation of a web application to predict diabetes disease: An approach using machine learning algorithm. Proceedings of the 2018 21st International Conference of Computer and Information Technology (ICCIT). 21- 23 December 2018 Dhaka, Bangladesh 1 23 10.1109/ICCITECHN.2018.8631968
   [Google Scholar]
10. Yuvaraj N. SriPreethaa KR. Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster. Cluster Comput 2019 22 S1 1 9 10.1007/s10586‑017‑1532‑x
    [Google Scholar]
11. Kannadasan K. Edla D.R. Kuppili V. Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clin. Epidemiol. Glob. Health 2019 7 4 530 535 10.1016/j.cegh.2018.12.004
    [Google Scholar]
12. Naz H. Ahuja S. Deep learning approach for diabetes prediction using PIMA Indian dataset. J. Diabetes Metab. Disord. 2020 19 1 391 403 10.1007/s40200‑020‑00520‑5 32550190
    [Google Scholar]
13. Tama B.A. Lee S. Comments on “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection”. Expert Syst. Appl. 2021 184 115488 10.1016/j.eswa.2021.115488
    [Google Scholar]
14. Madan P. Singh V. Chaudhari V. Albagory Y. Dumka A. Singh R. Gehlot A. Rashid M. Alshamrani S.S. AlGhamdi A.S. An optimization-based diabetes prediction model using CNN and Bi-directional LSTM in real-time environment. Appl. Sci. (Basel) 2022 12 8 3989 10.3390/app12083989
    [Google Scholar]
15. Haritha R. Babu D.S. Sammulal P. A hybrid approach for prediction of type-1 and type-2 diabetes using firefly and cuckoo search algorithms. Int. J. Appl. Eng. Res. 2018 13 2 896 907
    [Google Scholar]
16. Rahman M. Islam D. Mukti R.J. Saha I. A deep learning approach based on convolutional LSTM for detecting diabetes. Comput. Biol. Chem. 2020 88 107329 10.1016/j.compbiolchem.2020.107329 32688009
    [Google Scholar]
17. Bhopte M. Rai M. Hybrid deep learning CNN-LSTM model for diabetes prediction. Int. J. Sci. Res. 2022 8 1
    [Google Scholar]
18. Vhaduri S. Prioleau T. Adherence to personal health devices: A case study in diabetes management. Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare 02 February 2021, New York, NY, United States, pp. 62-72. 10.1145/3421937.3421977
    [Google Scholar]
19. Aslan M.F. Unlersen M.F. Sabanci K. Durdu A. CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection. Appl. Soft Comput. 2021 98 106912 10.1016/j.asoc.2020.106912 33230395
    [Google Scholar]
20. Liu H. Lang B. Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci. 2019 9 20 4396
    [Google Scholar]
21. Kotsiantis S.B. Decision trees: a recent overview. Artif. Intell. Rev. 2013 39 4 261 283 10.1007/s10462‑011‑9272‑4
    [Google Scholar]
22. Freire P.J. Osadchuk Y. Spinnler B. Napoli A. Schairer W. Costa N. Prilepsky J.E. Turitsyn S.K. Performance versus complexity study of neural network equalizers in coherent optical systems. J. Lightwave Technol. 2021 39 19 6085 6096 10.1109/JLT.2021.3096286
    [Google Scholar]
23. Xie S. Yu Z. Lv Z. Multi-disease prediction based on deep learning: A survey. CMES-Comp. Model. Engin. Sci 2021 128 2 016728 10.32604/cmes.2021.016728
    [Google Scholar]
24. Ashiquzzaman A Tushar AK Islam MR Reduction of overfitting in diabetes prediction using deep learning neural network. IT Convergence and Security 2017. Springer 2018 10.1007/978‑981‑10‑6451‑7_5
    [Google Scholar]
25. Viseu A. Integration of social science into research is crucial. Nature 2015 525 7569 291 291 10.1038/525291a 26381948
    [Google Scholar]