LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini

Mehmet Bilal Er; İbrahim Işık

doi:10.46810/tdfd.818528

Research Article

LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini

Year 2021, Volume: 10 Issue: 1, 68 - 74, 25.06.2021

Mehmet Bilal Er İbrahim Işık

https://doi.org/10.46810/tdfd.818528

Cited By: 5

Abstract

Diyabet, vücudun yeterli miktarda insülini üretmemesi veya iyi kullanamadığı durumda kan şekerinin normalin üstüne çıkması ile ortaya çıkan bir hastalıktır. Kan şekeri insanların ana enerji kaynağıdır ve bu enerji tüketilen yiyeceklerden gıdalardan gelir. Bu hastalık tedavi edilmez ise ölümcül olabilir. Ancak, erken tanı konulup tedaviye başlandığında tedavisi en olanaklı hastalıklardan biridir. Geleneksel diyabet teşhis süreci zorlu olduğundan, diyabetin klinik ve fiziksel verileri kullanılarak yapay sinir ağı, görüntü işleme ve derin öğrenme gibi sistemler kullanılarak hastalık teşhis edilebilmektedir. Bu araştırmada diyabet teşhisi için derin öğrenmeye dayalı bir model sunulmaktadır. Bu bağlamda Evrişimsel Sinir Ağı (ESA), Uzun Kısa Süreli Bellek (Long-short Term Memory Networks- LSTM) modelinin hibrit kullanımı sınıflandırma için tercih edilmiştir. Ayrıca ESA ve LSTM modelleri deneylerde ayrı ayrı kullanılmıştır. Önerilen modelin performansını değerlendirmek için literatürde yaygın olarak kullanılan Pima Indians Diabetes veri seti kullanılmıştır. En yüksek sınıflandırma başarısı %86,45 olarak ESA+LSTM modelinden elde edilmiştir.

Keywords

LSTM, Diyabet tahmini, Evrişimsel Sinir Ağ

References

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[4] S. Palaniappan and R. Awang, “Intelligent heart disease prediction system using data mining techniques,” 2008 IEEE/ACS International Conference on Computer Systems and Applications. IEEE, 2008, doi: 10.1109/aiccsa.2008.4493524.
[5] A. K. Dwivedi, “Analysis of computational intelligence techniques for diabetes mellitus prediction,” Neural Comput. Appl., vol. 30, no. 12, pp. 3837–3845, 2017, doi: 10.1007/s00521-017-2969-9.
[6] M. Heydari, M. Teimouri, Z. Heshmati, and S. M. Alavinia, “Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran,” Int. J. Diabetes Dev. Ctries., vol. 36, no. 2, pp. 167–173, 2015, doi: 10.1007/s13410-015-0374-4.
[7] S. G., V. R., and S. K.P., “Diabetes detection using deep learning algorithms,” ICT Express, vol. 4, no. 4, pp. 243–246, 2018, doi: 10.1016/j.icte.2018.10.005.
[8] N. Barakat, A. P. Bradley, and M. N. H. Barakat, “Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 4, pp. 1114–1120, 2010, doi: 10.1109/titb.2009.2039485.
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[11] M. Rahman, D. Islam, R. J. Mukti, and I. Saha, “A deep learning approach based on convolutional LSTM for detecting diabetes,” Comput. Biol. Chem., vol. 88, p. 107329, 2020, doi: 10.1016/j.compbiolchem.2020.107329.
[12] A. Massaro, V. Maritati, D. Giannone, D. Convertini, and A. Galiano, “LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction,” Appl. Sci., vol. 9, no. 17, p. 3532, 2019, doi: 10.3390/app9173532.
[13] N. Gill and P. Mittal, “A computational hybrid model with two level classification using SVM and neural network for predicting the diabetes disease,” vol. 87, pp. 1–10, May 2016.
[14] Y. Fu and C. Aldrich, “Flotation froth image recognition with convolutional neural networks,” Miner. Eng., vol. 132, pp. 183–190, 2019, doi: 10.1016/j.mineng.2018.12.011.
[15] A. GÜLCÜ and Z. KUŞ, “Konvolüsyonel Sinir Ağlarında Hiper-Parametre Optimizasyonu Yöntemlerinin İncelenmesi,” Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol. 7. Gazi Üniversitesi, pp. 503–522, 2019, doi: 10.29109/gujsc.514483.
[16] D. C. Cireundefinedan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, High Performance Convolutional Neural Networks for Image Classification,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Two, 2011, pp. 1237–1242.
[17] C. Olah, “Understanding LSTM Networks.” http://colah.github.io/posts/2015-08-Understanding-LSTMs/ (accessed Aug. 21, 2020).
[18] “Predict the Onset of Diabetes Based on Diagnostic Measures.” https://www.kaggle.com/uciml/pima-indians-diabetes-database (accessed Aug. 22, 2020).
[19] A. Ashiquzzaman et al., “Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network,” IT Convergence and Security 2017. Springer Singapore, pp. 35–43, 2017, doi: 10.1007/978-981-10-6451-7_5.
[20] S. M. H. Dadgar and M. Kaardaan, “A Hybrid Method of Feature Selection and Neural Network with Genetic Algorithm to Predict Diabetes,” 2017.
[21] R. Haritha, D. S. Babu, and D. P. Sammulal, “A Hybrid Approach for Prediction of Type-1 and Type-2 Diabetes using Firefly and Cuckoo Search Algorithms,” 2018.
[22] I. Esme, T. Hüseyin, I. İbrahim, "Analysis of thermoluminescence characteristics of a lithium disilicate glass ceramic using a nonlinear autoregressive with exogenous input model", Luminescence, 1-8, 2020.10.1002/bio.3788

Year 2021, Volume: 10 Issue: 1, 68 - 74, 25.06.2021

Mehmet Bilal Er İbrahim Işık

https://doi.org/10.46810/tdfd.818528

Cited By: 5

Abstract

References

[1] H. Naz and S. Ahuja, “Deep learning approach for diabetes prediction using PIMA Indian dataset,” J. Diabetes Metab. Disord., vol. 19, no. 1, pp. 391–403, Apr. 2020, doi: 10.1007/s40200-020-00520-5.
[2] F. Allam, Z. Nossai, H. Gomma, I. Ibrahim, and M. Abdelsalam, “A Recurrent Neural Network Approach for Predicting Glucose Concentration in Type-1 Diabetic Patients BT - Engineering Applications of Neural Networks,” 2011, pp. 254–259.
[3] A. Ramachandran, “Know the signs and symptoms of diabetes,” Indian J. Med. Res., vol. 140, pp. 579–581, Nov. 2014.
[4] S. Palaniappan and R. Awang, “Intelligent heart disease prediction system using data mining techniques,” 2008 IEEE/ACS International Conference on Computer Systems and Applications. IEEE, 2008, doi: 10.1109/aiccsa.2008.4493524.
[5] A. K. Dwivedi, “Analysis of computational intelligence techniques for diabetes mellitus prediction,” Neural Comput. Appl., vol. 30, no. 12, pp. 3837–3845, 2017, doi: 10.1007/s00521-017-2969-9.
[6] M. Heydari, M. Teimouri, Z. Heshmati, and S. M. Alavinia, “Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran,” Int. J. Diabetes Dev. Ctries., vol. 36, no. 2, pp. 167–173, 2015, doi: 10.1007/s13410-015-0374-4.
[7] S. G., V. R., and S. K.P., “Diabetes detection using deep learning algorithms,” ICT Express, vol. 4, no. 4, pp. 243–246, 2018, doi: 10.1016/j.icte.2018.10.005.
[8] N. Barakat, A. P. Bradley, and M. N. H. Barakat, “Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 4, pp. 1114–1120, 2010, doi: 10.1109/titb.2009.2039485.
[9] N. Yuvaraj and K. R. SriPreethaa, “Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster,” Cluster Comput., vol. 22, no. S1, pp. 1–9, 2017, doi: 10.1007/s10586-017-1532-x.
[10] H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes mellitus prediction model based on data mining,” Informatics Med. Unlocked, vol. 10, pp. 100–107, 2018, doi: 10.1016/j.imu.2017.12.006.
[11] M. Rahman, D. Islam, R. J. Mukti, and I. Saha, “A deep learning approach based on convolutional LSTM for detecting diabetes,” Comput. Biol. Chem., vol. 88, p. 107329, 2020, doi: 10.1016/j.compbiolchem.2020.107329.
[12] A. Massaro, V. Maritati, D. Giannone, D. Convertini, and A. Galiano, “LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction,” Appl. Sci., vol. 9, no. 17, p. 3532, 2019, doi: 10.3390/app9173532.
[13] N. Gill and P. Mittal, “A computational hybrid model with two level classification using SVM and neural network for predicting the diabetes disease,” vol. 87, pp. 1–10, May 2016.
[14] Y. Fu and C. Aldrich, “Flotation froth image recognition with convolutional neural networks,” Miner. Eng., vol. 132, pp. 183–190, 2019, doi: 10.1016/j.mineng.2018.12.011.
[15] A. GÜLCÜ and Z. KUŞ, “Konvolüsyonel Sinir Ağlarında Hiper-Parametre Optimizasyonu Yöntemlerinin İncelenmesi,” Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol. 7. Gazi Üniversitesi, pp. 503–522, 2019, doi: 10.29109/gujsc.514483.
[16] D. C. Cireundefinedan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, High Performance Convolutional Neural Networks for Image Classification,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Two, 2011, pp. 1237–1242.
[17] C. Olah, “Understanding LSTM Networks.” http://colah.github.io/posts/2015-08-Understanding-LSTMs/ (accessed Aug. 21, 2020).
[18] “Predict the Onset of Diabetes Based on Diagnostic Measures.” https://www.kaggle.com/uciml/pima-indians-diabetes-database (accessed Aug. 22, 2020).
[19] A. Ashiquzzaman et al., “Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network,” IT Convergence and Security 2017. Springer Singapore, pp. 35–43, 2017, doi: 10.1007/978-981-10-6451-7_5.
[20] S. M. H. Dadgar and M. Kaardaan, “A Hybrid Method of Feature Selection and Neural Network with Genetic Algorithm to Predict Diabetes,” 2017.
[21] R. Haritha, D. S. Babu, and D. P. Sammulal, “A Hybrid Approach for Prediction of Type-1 and Type-2 Diabetes using Firefly and Cuckoo Search Algorithms,” 2018.
[22] I. Esme, T. Hüseyin, I. İbrahim, "Analysis of thermoluminescence characteristics of a lithium disilicate glass ceramic using a nonlinear autoregressive with exogenous input model", Luminescence, 1-8, 2020.10.1002/bio.3788

There are 22 citations in total.

Details

Primary Language	Turkish
Subjects	Engineering
Journal Section	Articles
Authors	Mehmet Bilal Er 0000-0002-2074-1776 İbrahim Işık 0000-0003-1355-9420
Publication Date	June 25, 2021
Published in Issue	Year 2021 Volume: 10 Issue: 1

Cite

APA	Er, M. B., & Işık, İ. (2021). LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini. Türk Doğa Ve Fen Dergisi, 10(1), 68-74. https://doi.org/10.46810/tdfd.818528
AMA	Er MB, Işık İ. LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini. TJNS. June 2021;10(1):68-74. doi:10.46810/tdfd.818528
Chicago	Er, Mehmet Bilal, and İbrahim Işık. “LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini”. Türk Doğa Ve Fen Dergisi 10, no. 1 (June 2021): 68-74. https://doi.org/10.46810/tdfd.818528.
EndNote	Er MB, Işık İ (June 1, 2021) LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini. Türk Doğa ve Fen Dergisi 10 1 68–74.
IEEE	M. B. Er and İ. Işık, “LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini”, TJNS, vol. 10, no. 1, pp. 68–74, 2021, doi: 10.46810/tdfd.818528.
ISNAD	Er, Mehmet Bilal - Işık, İbrahim. “LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini”. Türk Doğa ve Fen Dergisi 10/1 (June 2021), 68-74. https://doi.org/10.46810/tdfd.818528.
JAMA	Er MB, Işık İ. LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini. TJNS. 2021;10:68–74.
MLA	Er, Mehmet Bilal and İbrahim Işık. “LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini”. Türk Doğa Ve Fen Dergisi, vol. 10, no. 1, 2021, pp. 68-74, doi:10.46810/tdfd.818528.
Vancouver	Er MB, Işık İ. LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini. TJNS. 2021;10(1):68-74.