Next Article in Journal
Detection of a Semi-Rough Target in Turbulent Atmosphere by an Electromagnetic Gaussian Schell-Model Beam
Previous Article in Journal
Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks
Article Menu

Export Article

Open AccessArticle

Data Driven Approach for Eye Disease Classification with Machine Learning

1
Department of Computer Science, Lahore College for Women University, Lahore 54000, Pakistan
2
Software Research Institute, Athlone Institute of Technology, N37FW63 Athlone, Ireland
3
Institute of Ophthalmology, Mayo Hospital, King Edward Medical University, Lahore 54000, Pakistan
4
The Eye Associates, 2 Zafar Ali Road, Lahore 54000, Pakistan
5
School of Science, Technology and Engineering, University of Suffolk, Ipswich IP4 1QJ, UK
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2789; https://doi.org/10.3390/app9142789
Received: 21 May 2019 / Revised: 5 July 2019 / Accepted: 8 July 2019 / Published: 11 July 2019
(This article belongs to the Section Computing and Artificial Intelligence)
  |  
PDF [1310 KB, uploaded 11 July 2019]
  |  

Abstract

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm. View Full-Text
Keywords: Machine Learning; Classification; Framework; Eye diseases; ICD codes Machine Learning; Classification; Framework; Eye diseases; ICD codes
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Malik, S.; Kanwal, N.; Asghar, M.N.; Sadiq, M.A.A.; Karamat, I.; Fleury, M. Data Driven Approach for Eye Disease Classification with Machine Learning. Appl. Sci. 2019, 9, 2789.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top