# Data Driven Approach for Eye Disease Classification with Machine Learning
## 2 Related Work  
A large amount of research has been conducted on developing medical expert systems to automate diagnostic processes [2; 4; 10; 11; 12]. These expert systems can produce accurate responses based on pre-defined rules; however, the use of static rules results in restricted learning and therefore failure to respond to new situations. With the advancement in machine learning algorithms, the focus shifted toward machine learning through training data. Hence, almost all medical fields, in particular ophthalmology, are now experiencing rapid growth in machine-based research activities. A brief review of related work will now be provided. In References [4; 13; 14; 15; 16], textual and numeric data was used for certain eye problems, namely, dry eye diseases, refractive error, esotropic eyes and progression in glaucomatous visual field defects. Moreover, probabilistic classifiers such as naive Bayes and support vector machines were used. In 2011, research on cataract disease was conducted by the Electronic Medical Records and Genomics Network. ICD-9 (International Classification of Diseases) [17] codes and current procedural terminology codes were used for cataract diagnosis in patients without surgery. Furthermore, natural language processing (NLP) and intelligent character recognition (ICR) were the machine-learning techniques used to identify cataract cases and their type. Specifically, written documents were scanned and after ICR and NLP were performed, they were used in the diagnosis process [18; 19]. In 2014, a study was conducted to analyze clinical data for finding the relationship between signs and diagnosis of dry eyes disease [20]. The dataset was obtained from 344 patients and the analysis was based on independent component analysis (ICA) and Pearson correlations. The highest correlations were found between conjunctiva and corneal staining. Furthermore, each component of the ICA mixing matrix exhibited minimal residual information. Consequently, no consistent relationship was found among the most frequently used signs and symptoms. Moreover, a number of studies focused on analyzing image data for direct conversion into diagnostic data. In 2018, research was conducted into blood vessel segmentation [21] using image-processing methods based on machine-learning methods. Vessel segmentation process is divided into four categories including vessel enhancement, machine learning, deformable models and tracking. Reference [21] also provides a recent survey of the methods that can be used in this type of research and Reference [22] provides an earlier such survey. The most common disease leading to blindness is glaucoma [1], for which image-based detection was relatively more efficient than for other diseases. In References [3; 23; 24; 25], algorithms were used for the detection of abnormal retina along with the classification of a normal retina. Furthermore, multiple diagnostic images of the retinas were used along with patient clinical records. In Reference [26], a survey of various computer-based ocular disease identification methods was presented. Multilayer perceptron, support vector machines and linear and quadratic discriminant classifiers were found to be more successful in identifying different eye problems. As previously mentioned, the most common but dangerous eye disease is currently glaucoma. Optical coherence tomography (OCT) images are used for its diagnosis and therefore they should be classified for computer-aided glaucoma detection [1; 6; 27; 28; 29; 30; 31]. Similarly, in Reference [32], imaging data was used for identifying intraocular lenses and refractive surgery. EyeView was used to optimize vision quality. Furthermore, computing solutions have been presented for identifying specific eye diseases such as age-related macular degradation [33], for the auto-detection of a diabetic retina [34; 35] and for automatic localization of the optic disc using image classification with support vector machines [5].