Skip to content
2000
Volume 21, Issue 10
  • ISSN: 1573-3998
  • E-ISSN: 1875-6417

Abstract

Background

Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.

Methods

In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.

Results

The results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (., CNN+MLP model) outperforms other models with an accuracy of 93.89%.

Conclusion

With these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.

Loading

Article metrics loading...

/content/journals/cdr/10.2174/0115733998307556240819093038
2024-11-29
2025-10-31
Loading full text...

Full text loading...

References

  1. RoglicG. Global Report on Diabetes.Available from:https://www.who.int/publications/i/item/9789241565257(accessed on 6-8-2024)2016
  2. MurphyZ. Diabetes: Asia’s 'silent killer.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)2013
  3. AllamF. NossaiZ. GommaH. IbrahimI. AbdelsalamM. A recurrent neural network approach for predicting glucose concentration in type-1 diabetic patients.In: Engineering Applications of Neural Networks.Berlin, HeidelbergSpringer201125425910.1007/978‑3‑642‑23957‑1_29
    [Google Scholar]
  4. NasserA.R. HasanA.M. HumaidiA.J. Iot and cloud computing in health-care: A new wearable device and cloud-based deep learning algorithm for monitoring of diabetes.Electronics (Basel)20211021271910.3390/electronics10212719
    [Google Scholar]
  5. YamashitaR. NishioM. DoR.K.G. TogashiK. Convolutional neural networks: an overview and application in radiology.Insights Imaging20189461162910.1007/s13244‑018‑0639‑9 29934920
    [Google Scholar]
  6. SwapnaG VinayakumarR SomanKP Diabetes detection using deep learning algorithms.ICT express2018442436
    [Google Scholar]
  7. NieminenJ. GomezC. IsomakiM. Networking solutions for connecting bluetooth low energy enabled machines to the internet of things.IEEE Netw.2014286839010.1109/MNET.2014.6963809
    [Google Scholar]
  8. Pima Indians Diabetes Database.Available from:https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database(accessed on 6-8-2024)2016
  9. DeyS.K. HossainA. RahmanM.M. 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, pp. 1-23.10.1109/ICCITECHN.2018.8631968
    [Google Scholar]
  10. YuvarajN. SriPreethaaK.R. Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster.Cluster Comput.201922S11910.1007/s10586‑017‑1532‑x
    [Google Scholar]
  11. KannadasanK. EdlaD.R. KuppiliV. Type 2 diabetes data classification using stacked autoencoders in deep neural networks.Clin. Epidemiol. Glob. Health20197453053510.1016/j.cegh.2018.12.004
    [Google Scholar]
  12. NazH. AhujaS. Deep learning approach for diabetes prediction using PIMA Indian dataset.J. Diabetes Metab. Disord.202019139140310.1007/s40200‑020‑00520‑5 32550190
    [Google Scholar]
  13. TamaB.A. LeeS. Comments on “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection”.Expert Syst. Appl.202118411548810.1016/j.eswa.2021.115488
    [Google Scholar]
  14. MadanP. SinghV. ChaudhariV. An optimization-based diabetes prediction model using CNN and Bi-directional LSTM in real-time environment.Appl. Sci. (Basel)2022128398910.3390/app12083989
    [Google Scholar]
  15. HarithaR. BabuD.S. SammulalP. A hybrid approach for prediction of type-1 and type-2 diabetes using firefly and cuckoo search algorithms.Int. J. Appl. Eng. Res.2018132896907
    [Google Scholar]
  16. RahmanM. IslamD. MuktiR.J. SahaI. A deep learning approach based on convolutional LSTM for detecting diabetes.Comput. Biol. Chem.20208810732910.1016/j.compbiolchem.2020.107329 32688009
    [Google Scholar]
  17. BhopteM. RaiM. Hybrid deep learning CNN-LSTM model for diabetes prediction.Int. J. Sci. Res.202281
    [Google Scholar]
  18. VhaduriS. PrioleauT. Adherence to personal health devices: A case study in diabetes management.Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare02 February 2021, New York, NY, United States, pp. 62-72.10.1145/3421937.3421977
    [Google Scholar]
  19. AslanM.F. UnlersenM.F. SabanciK. DurduA. CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection.Appl. Soft Comput.20219810691210.1016/j.asoc.2020.106912 33230395
    [Google Scholar]
  20. LiuH. LangB. Machine learning and deep learning methods for intrusion detection systems: A survey.Appl. Sci.20199204396
    [Google Scholar]
  21. KotsiantisS.B. Decision trees: a recent overview.Artif. Intell. Rev.201339426128310.1007/s10462‑011‑9272‑4
    [Google Scholar]
  22. FreireP.J. OsadchukY. SpinnlerB. Performance versus complexity study of neural network equalizers in coherent optical systems.J. Lightwave Technol.202139196085609610.1109/JLT.2021.3096286
    [Google Scholar]
  23. XieS. YuZ. LvZ. Multi-disease prediction based on deep learning: A survey.CMES-Comp Model Engin Sci2021128201672810.32604/cmes.2021.016728
    [Google Scholar]
  24. AshiquzzamanA. TusharA.K. IslamM.R. Reduction of overfitting in diabetes prediction using deep learning neural network. In: IT Convergence and Security 2017Springer201810.1007/978‑981‑10‑6451‑7_5
    [Google Scholar]
  25. ViseuA. Integration of social science into research is crucial.Nature20155257569291110.1038/525291a 26381948
    [Google Scholar]
/content/journals/cdr/10.2174/0115733998307556240819093038
Loading
/content/journals/cdr/10.2174/0115733998307556240819093038
Loading

Data & Media loading...


  • Article Type:
    Research Article
Keyword(s): Complexity; deep learning; diabetes; heart problems; internet of things; neurological damage
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test