Detection Diabetic Retinopathy with Supervised Learning
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Abstract
Diabetic retinopathy is a common complication that occurs in people with diabetes mellitus. Diabetic retinopathy damage is characterized in the blood vessel system in the layer at the back of the eye, especially in tissues that respond to light. This research aims to detect diabetic retinopathy early by using SVM and Random forest. SVM is a classification technique that divides the input space into two classes. Random Forest is a supervised learning algorithm that utilizes a collection of decision trees trained using the bagging method. This research uses datasets from diaretdb1 and messidor to evaluate the performance of both methods. The diaretdb1 dataset consists of 178 data points with the diagnosis of Proliferative Diabetic Retinopathy and Non-Diabetic Retinopathy. In addition, the messidor dataset consists of 105 data points with the diagnosis of Diabetic Retinopathy and Non-Diabetic Retinopathy. Experimental results on the diaretdb1 dataset showed that SVM achieved 88% accuracy, while Random Forest achieved 91% accuracy. Similarly, on the messidor dataset, SVM achieved 80% accuracy, while Random Forest achieved 85% accuracy.
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References
K.K.M. Rahman, M. Nasor and A. Imran, (2022). “Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms”. Diagnostics, vol. 12, no. 9, pp.1132-1141. DOI: https://doi.org/10.3390/diagnostics12092262
B. Sumathy, S. Gupta, S.S. Hishan, B. Raj, K. Gulati, G. Dhiman, (2022). “Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science”. International Journal of Reliable and Quality E-Healthcare, vol. 11, no. 2, pp.1-16. DOI: https://doi.org/10.4018/IJRQEH.299959
P.M. Karpecki, (2015). Kanski’s Clinical Ophthalmology: A Systematic Approach, Brad Bowling, LWW, Lippincott Williams & Wilkins, Philadelphia, Pennsylvania, LWM. DOI: https://doi.org/10.1097/OPX.0000000000000737
Omer Faruk Gurcan, Omer Faruk Beyca, Onur Dogan. (2021). “A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification”, International Journal of Computational Intelligence Systems, vol. 14, no. 2, pp.1132-1141. DOI: https://doi.org/10.2991/ijcis.d.210316.001
J.A.M. Sidey-Gibbons, and C.J. Sidey-Gibbons, (2019). Machine learning in medicine: A practical introduction. BMC Med Res. Methodol. 64. DOI: https://doi.org/10.1186/s12874-019-0681-4
A. Roihan, P.A. Sunarya, and A.S. Rafika, (2020). “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper”, IJCIT (Indonesian Journal on Computer and Information Technology), vol. 5, no. 1, pp.75-82. DOI: https://doi.org/10.31294/ijcit.v5i1.7951
H. Al Azies, and G. Anuraga, (2021). “Klasifikasi Daerah Tertinggal di Indonesia Menggunakan Algoritma SVM dan K-NN”. Jurnal ILMU DASAR, vol. 22, no. 1, pp.31-38 DOI: https://doi.org/10.19184/jid.v22i1.16928
L. Wang, H. Liu, Y. Lu, H. Chen, J. Zhang and J. Pu. (2019). “A Coarse-to-Fine Deep Learning Framework for Optic Disc Segmentation in Fundus Images”, Biomedical Signal Processing and Control, Vol. 51, pp. 82-89. DOI: https://doi.org/10.1016/j.bspc.2019.01.022
A.K. Whardana, and H. Tjandrasa, (2014). “Segmentasi Microaneurysm Pada Citra Fundus Retina Untuk Deteksi Dini Diabetic Retinopathy” Scan, vol. 9, no. 3, pp.49-54.
R.H.B. Saputra, and R. Mubarok, (2022). “Implementasi Algoritma Random Forest Untuk Mendiagnosis Kejadian Berulang (Kekambuhan) Pada Kanker Payudara Berbasis Web”, OKTAL : Jurnal Ilmu Komputer dan Sains, vol. 1, no. 6, pp.564-572.
F.A. Novianti, and S.W. Purnami, (2012). “Analisis Diagnosis Pasien Kanker Payudara Menggunakan Regresi Logistik dan Support Vector Machine (SVM) Berdasarkan Hasil Mamografi”. Jurnal Sains Dan Seni ITS, vol. 1, no. 1, pp.147-152.
P. Saimadhu, (2017). How the Random Forest Algorithm Works in Machine Learning, Published on May 22.
B. Lowe, and A. Kulkarni. (2015). “Multispectral Image Analysis Using Random Forest”, International Journal on Soft Computing (IJSC), vol. 6, no. 1, pp.1-14. DOI: https://doi.org/10.5121/ijsc.2015.6101
R. Casanova, S. Saldana, E.Y. Chew, R.P. Danis, C.M. Greven, W.T. Ambrosius, (2014). “Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses”, Plos One, vol. 9, no. 6, doi:10.1371/journal.pone.0098587 DOI: https://doi.org/10.1371/journal.pone.0098587
M. Gandhi, and R. Dhanasekaran, (2013). Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifier, in Proceedings of IEEE International conference on Communication and Signal Processing, pp.873-877, doi: 10.1109/iccsp.2013.6577181. DOI: https://doi.org/10.1109/iccsp.2013.6577181