A Machine Learning Approach for the Diagnosis of Diabetes : A Review

Authors

  • Pravin S. Rahate  PhD Scholar, Department of CSE, MPU, Bhopal, Madhya Pradesh, India
  • Dr. Nikhat Raza  Associate Professor Department of CSE MPU, Bhopal, Madhya Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT2062141

Keywords:

Machine Learning, Diabetic Macular, Diabetic Retinopathy, Image Screening.

Abstract

Diabetes mellitus (DM) is a chronic disease that affects 382 million patients’ worldwide (2013 data) and is predicted to increase to as many as 592 million adults by 2035. DM is one of the major causes of blindness in young adults around the world. The most serious ocular complication of DM is diabetic retinopathy (DR).Diabetic retinopathy is the most common microvascular complication in diabetes1, for the screening of which the retinal imaging is the most widely used method due to its high sensitivity in detecting retinopathy. Prompt diagnosis is important through efficient screening. The evaluation of the severity and degree of retinopathy associated with a person having diabetes is currently performed by medical experts based on the fundus or retinal images of the patient’s eyes As the number of patients with diabetes is rapidly increasing, the number of retinal images produced by the screening programmes will also increase, which in turn introduces a large labor-intensive burden on the medical experts as well as cost to the healthcare services. Manual grading of these images to determine the severity of DR is rather slow and resource demanding. This could be alleviated with an automated system either as support for medical experts’ work or as full diagnosis tool. This labor-intensive task could greatly benefit from automatic detection using machine learning technique. Early detection and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. The objective of this paper is to explore the work happening on the detection, progression and feature selection process for the prediction of DR and to establish the extent and depth of existing knowledge on RD prediction process.

References

  1. www.arogyaworld.org
  2. Md. Faisal Faruque ; Asaduzzaman ; Iqbal H. Sarker ,"Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus", International Conference on Electrical, Computer and Communication Engineering (ECCE),2019
  3. Sajratul Yakin Rubaiat ; MdMoniborRahman ; Md. KamrulHasan , "Important Feature Selection & Accuracy Comparisons of Different Machine Learning Models for Early Diabetes Detection",International Conference on Innovation in Engineering and Technology (ICIET),2018 
  4. MichealDutt ; VimalaNunavath ;" Morten Goodwin,A Multi-layer Feed Forward Neural Network Approach for Diagnosing Diabetes", 11th International Conference on Developments in eSystems Engineering (DeSE),2018 5.FikirteGirmaWoldemichael ; SumitraMenaria ,"Prediction of Diabetes Using Data Mining Techniques", 2nd International Conference on Trends in Electronics and Informatics (ICOEI),2018 
  5. Jiangxue Han ; Wenping Jiang ; Cuixia Dai ; Hongyan Ma, "The Design of Diabetic Retinopathy Classifier Based on Parameter Optimization SVM", International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS),2018
  6. Mohamed Chetoui ; Moulav A. Akhloufi ; MustanhaKardouchi, "Diabetic Retinopathy Detection Using Machine Learning and Texture Features" , IEEE Canadian Conference on Electrical & Computer Engineering (CCECE),2018 8.NurselYalçin ; SeyfullahAlver ; NeclaUluhatun, "Classification of retinal images with deep learning for early detection of diabetic retinopathy disease",26th Signal Processing and Communications Applications Conference (SIU),2018 
  7. R. GeethaRamani ; JeslinShanthamalar J ; Lakshmi B,"Automatic Diabetic Retinopathy Detection Through Ensemble Classification Techniques Automated Diabetic Retionapthy Classification", IEEE International Conference on Computational Intelligence and Computing Research (ICCIC),2017 10.ShreyaAliwadi ; VrindaShandila ; TanishaGahlawat ; ParulKalra ; DeeptiMehrotra, "Diagnosis of diabetic nature of a person using SVM and ANN approach", 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO),2017 
  8. Maham Jahangir ; HammadAfzal ; Mehreen Ahmed ; KhawarKhurshid ; RaheelNawaz,"An expert system for diabetes prediction using auto tuned multi-layer perceptron", Intelligent Systems Conference (IntelliSys),2017 
  9. Ephzibah, E.P. "Cost Effective Approach on Feature Selection using Genetic Algorithms and Fuzzy Logic for Diabetes Diagnosis". International Journal on Soft Computing (IJSC), 2011, 2, 1-10. 
  10. Kumari, V.A. and Chitra, R."Classification of Diabetes Disease Using Support Vector Machine.", International Journal of Engineering Research and Applications (IJERA), 2013, 3, 1797-1801. 
  11. Sen, S.K. and Dash, S. "Application of Meta Learning Algorithms for the Prediction of Diabetes Disease.", International Journal of Advance Research in Computer Science and Management Studies, 2014, 2, 396-401. 
  12. Imran Qureshi , Jun Ma, and Qaisar Abbas," Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy", Symmetry 2019, 11, 749; doi:10.3390/sym11060749 
  13. Pedro Romero-Aroca, Aida Valls, Antonio Moreno,RamonSagarra-Alamo, JosepBasora-Gallisa, EmranSaleh, Marc Baget-Bernaldiz, DomenecPuig, "A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application ",Telemed J E Health. 2019 Jan 1; 25(1): 31–40. 
  14. JaakkoSahlsten, Joel Jaskari, JyriKivinen, LauriTurunen, EsaJaanio, KustaaHietala&KimmoKaski, "Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading", Scientific Reports | (2019) 9:10750 | https://doi.org/10.1038/s41598-019-47181-w 
  15. Sajib Kumar Saha , Di Xiao , AlauddinBhuiyan , Tien Y. Wong , YogesanKanagasingam "Color fundus image registration techniques and applications for automated analysis of diabetic retinopathy progression: A review", Biomedical Signal Processing and ControlVolume 47, January 2019, Pages 288-302

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-04-30

Issue

Volume 6, Issue 2, March-April-2020

Section

Research Articles

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How to Cite

[1]
Pravin S. Rahate, Dr. Nikhat Raza, " A Machine Learning Approach for the Diagnosis of Diabetes : A Review , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.509-516, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT2062141