# Data Driven Approach for Eye Disease Classification with Machine Learning
## 3 Methodology Adopted
### Data Modeling  
To resolve the ambiguity caused by variations in medical language when representing the same concepts, controlled terminologies have been established, ensuring effective communication across health care entities and information systems. These terminologies map synonyms of medical terms to a common concept so that similar objects can be grouped together, thereby providing the infrastructure to support powerful features such as retrospective data analysis, prospective clinical trials and evidence-based practice [7]. Various standards in medical terminologies are available, such as the 10 th version of International Classification of Diseases (ICD-10), Systematized Nomenclature of Medicine, Clinical Terms and Medical Entities Dictionary [7]. In ophthalmology, multiple factors are considered, such as illness history (including general health information), anterior eye examination (through a slit lamp) and posterior segment examination (through a specialized lens). Therefore, data from slit lamp examination and posterior segment examination are combined for diagnosis, as shown in Figure 1. ICD-10 [17] is also based on structured hierarchies for eye-related disease diagnosis. Therefore, these examinations were used for AI-based learning and were arranged in a hierarchical structure similar to the physician decision tree shown in Figure 2. After rigorous analysis and discussion with experts, the most suitable hierarchical structures of ICD-10 coding for eye diseases were adopted. Diagnosis consisted of multiple levels, according to the existing eye problem. There were a maximum of six levels involved in the definition of any symptom. However, for flexibility, less than six hierarchical levels were required for cases where the disease is at a premature stage. Therefore, in the proposed framework, the definition of a symptom varies according to the situation. An example of a symptom identified from slit examination can be seen below. Cornea \(\rightarrow\) Endothelium \(\rightarrow\) KPs \(\rightarrow\) Fine \(\rightarrow\) Site \(\rightarrow\) Generalized  
Moreover, recording data in this form is impossible during the examination, as the doctor cannot ignore the patient while writing down all required elements of a symptom. Therefore, a highly efficient method is required to ensure accurate data entry and, hence, an interface for web applications was developed so that the doctor or any other medical expert needs to click only three times (on average) to select the entire hierarchy of a particular symptom. Figure 3 shows the definition of a symptom using a graphical user interface. The mouse is pointed to the plus symbol of \"Cornea\" for either the right or the left eye and the next level of available options appears as a dropdown menu. When the user further hovers the mouse over \"Endothelium\" the next level of available options appears. Continuing further, multiple options appear for the final definition of the symptom. Finally, the last option is  
Figure 1: Eye disease diagnosis mechanism. Figure 2: ICD-10 physician decision tree for diabetic retinopathy [48]. selected and all the above levels are concatenated. In the example, when \"Generalized\" is selected, the complete hierarchy will be displayed. Furthermore, a graphical user interface was also developed for providing a diagnosis in standard format. This is achieved by introducing a search option in the diagnosis dropdown menu because it is difficult to scroll through all available options to reach the required diagnosis. After entering the keywords, the user clicks on the desired diagnosis in a standard format, as shown in Figure 4. This approach has the advantage of obtaining the same data format from all users. Figure 4: Data entry of diagnosis through search engine: The initial letters of the diagnosis are entered and the system automatically suggests the related diagnosis, thus reducing the effort in typing or selecting multiple hierarchies.