Deteksi Diabetik Retinopati menggunakan Regresi Logistik


Raras Tyasnurita(1*); Adhi Yoga Muris Pamungkas(2);

(1) Institut Teknologi Sepuluh
(2) Institut Teknologi Sepuluh
(*) Corresponding Author

  

Abstract


Retinopathy diabetic is a disease caused by diabetes mellitus complications that can cause damage to the retina of the eye. It has a direct impact on the disruption of the vision of the patient. Detecting this disease is very important to prevent total blindness on diabetes mellitus patients. One method to do the detection is by using machine learning. This research uses feature extraction data from an image that contains signs of retinopathy diabetic or not. In this research, we focus on retinopathy diabetic classification. We applied logistic regression algorithm for classification. There is four training condition in a machine learning model: using the default parameter, feature standardization, feature selection, and hyper-parameter tuning. The model used a regularization control (C) value of 11.288, iterations 200, and a regularization penalty (l1). The experimental results show that this proposed model with full features produced 80,17% accuracy in data validation.

Keywords


Retinopathy diabetic; Logistic regression; Classification; Machine learning

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 596 times
PDF view: 491 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v12i2.578.130-135
  

Cite

References


Y. Yuhelma, Y. Hasneli I, and F. Annis N, “Identifikasi dan Analisis Komplikasi Makrovaskuler dan Mikrovaskuler pada Pasien Diabetes Mellitus,” J. Online Mhs., vol. 2, no. 1, pp. 569–579, 2015.

R. Casanova, S. Saldana, E. Y. Chew, R. P. Danis, C. M. Greven, and W. T. Ambrosius, “Application of random forests methods to diabetic retinopathy classification analyses,” PLoS One, vol. 9, no. 6, pp. 1–8, 2014, doi: 10.1371/journal.pone.0098587.

P. Soewondo, S. Soegondo, K. Suastika, A. Pranoto, D. W. Soeatmadji, and A. Tjokroprawiro, “Medical journal of Indonesia,” Med. J. Indones., vol. 19, no. 4, pp. 235–44, 2010, [Online]. Available: http://mji.ui.ac.id/journal/index.php/mji/article/view/412/404.

G. T. Reddy et al., “An ensemble based machine learning model for diabetic retinopathy classification,” in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020, pp. 1–6.

E. Christodoulou, J. Ma, G. S. Collins, E. W. Steyerberg, J. Y. Verbakel, and B. Van Calster, “A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models,” J. Clin. Epidemiol., vol. 110, pp. 12–22, 2019, doi: 10.1016/j.jclinepi.2019.02.004.

Y. F. Kao and R. Venkatachalam, “Human and Machine Learning,” Comput. Econ., no. February, pp. 1–21, 2018, doi: 10.1007/s10614-018-9803-z.

G. F. Cooper et al., “An evaluation of machine-learning methods for predicting pneumonia mortality,” Artif. Intell. Med., vol. 9, no. 2, pp. 107–138, 1997, doi: 10.1016/S0933-3657(96)00367-3.

T. Sasakawa, J. Hu, and K. Hirasawa, “A brainlike learning system with supervised, unsupervised, and reinforcement learning,” Electr. Eng. Japan, vol. 162, no. 1, pp. 32–39, 2008.

G. Varoquaux, L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa, and A. Mueller, “Scikit-learn: machine learning without learning the machinery,” GetMobile Mob. Comput. Commun., vol. 19, no. 1, pp. 29–33, 2015, doi: 10.1145/2786984.2786995.

B. Antal and A. Hajdu, “Diabetic Retinopathy Debrecen Data Set,” UCI Mach. Learn. Repos., 2014.

S. Mohammadian, A. Karsaz, and Y. M. Roshan, “A comparative analysis of classification algorithms in diabetic retinopathy screening,” Proc. Int. Conf. Softw. Eng. Knowl. Eng. SEKE, vol. 60, no. 10, p. 631, 2017, doi: 10.18293/SEKE2017-207.

G. Luo, “A review of automatic selection methods for machine learning algorithms and hyper-parameter values,” Netw. Model. Anal. Heal. Informatics Bioinforma., vol. 5, no. 1, pp. 1–15, 2016, doi: 10.1007/s13721-016-0125-6.

G. King and L. Zeng, “Logistic regression in rare events data,” Polit. Anal., vol. 9, no. 2, pp. 137–163, 2001.

S. C. Bagley, H. White, and B. A. Golomb, “Logistic regression in the medical literature: Standards for use and reporting, with particular attention to one medical domain,” J. Clin. Epidemiol., vol. 54, no. 10, pp. 979–985, 2001, doi: 10.1016/S0895-4356(01)00372-9.

C. Y. J. Peng, K. L. Lee, and G. M. Ingersoll, “An introduction to logistic regression analysis and reporting,” J. Educ. Res., vol. 96, no. 1, pp. 3–14, 2002, doi: 10.1080/00220670209598786.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Raras Tyasnurita, Adhi Yoga Muris Pamungkas

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.