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medicine and are plagued with inadequacy of expertise for development of their knowledge base.
Thirdly, the knowledge base in emerging medical fields is volatile and unstable and could make it less attractive to early researchers.
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process and with expectations of future events that set CASNET apart from the other early AIM systems. To make reliable diagnosis with a system requires that the patient or his guide must be able to make valid entry as requested by the diagnostic system. And as earlier stated in the theoretical background in this review, one of such entries must include the visual acuity value of the patient.
CASNET as an expert system lacked this very important feature. Previous attempts to design an eye diagnostic system failed to couple expert system technology with automated visual acuity measurement system.
2.10.2 Experimental System for Self-Diagnosing of Eye Diseases (ESSDED)
Kurniawan et al.(2014), discussed an experimental online expert system designed for eye diagnosis based on Naive Bayes model. The developed expert system applies Case-Based Reasoning (CBR), which is a paradigm for reasoning from experience while the Naïve Bayes is used as a method for classifying eye diseases by applying Bayes' theorem. The output of the expert system was a classification of an eye disease and information on the best treatment option. Comparison of expected versus actual results using the system revealed 82% accuracy. Kurniawan, therefore concluded that expert system using Naïve Bayes methodology has a potential to be used effectively in eye diagnostic tool, although it still has lots of challenges yet to be overcome by the system, being still at experimental stage. It shall be noted that this system does not test the visual acuity of the patient as a confirmatory tool for validating patient‘s response.
2.10.3 Expert System for Early Diagnosis of Eye Diseases (ESEDED)
Fatimah et al.(2001), reported the development of a ruled based expert system for detecting various eye diseases in Malaysia. The system can detect five prevalent eye diseases in Malaysia which include the following: allergic or infectious conjunctivitis, secondary and senile cataract, open angle glaucoma and
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acute glaucoma, keratitis and dry eyes syndrome. The project was designed and programmed via the object-oriented expert system shell software, EXSYS. Expert rules were developed based on the symptoms of each type of the eye diseases, and they were presented using a tree graph forward chaining with depth search first method. In order to enhance user interaction with the system, graphical user interfaces were employed. Although previous several similar works have been published, they are limited to detecting a single eye disease and also required expert medical officer to operate. This system is promising although the number of diseases handled is low when compared to hundreds of existing eye diseases. Moreover, the system is restricted as it does not have open architecture such that more diseases could be added by the expert. Once more the visual acuity of the patient was not taken into consideration as a confirmatory tool for validating patient‘s response.
2.10.4 Expert System for Diagnosing Eye Diseases Using Clips (ESDEDUC)
Naser & Zaiter (2008) reported on the design of a rule based expert system for eye diagnosis using CLIPS language. An initial evaluation of the expert system was carried out and a positive feedback was received from the users. According to the report, CLIPS program is used by reason of its flexibility, expandability and low cost. The knowledge acquisition followed the routine and known standard approach. The scope of the expert system covered four eye diseases: Eye discharges, Bulging Eye, Double Vision, and Drooping Eyelid. The system will conclude the eye disease diagnosis based on answers given by the patient to specific question asked by the system. The inference engine uses a forward chaining technique to match the questions with the corresponding symptoms, as answered by the patient, by relying on the facts from the facts and rules in the knowledge base. An initial evaluation of the expert system was done by doctors and patients. A number of doctors and patients tested the system and gave a positive feedback but commented on the low scope of the system, a factor that hunts
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most of the expert systems that were tried on eye diagnosis. Unfortunately, this system does neither test nor use the visual acuity of the patient as a confirmatory tool for validating patient‘s response.
2.10.5 Neural Networks and Decision Trees for Eye Diseases Diagnosis (NNDTEDD)
Kabari & Nwachukwu (2013) also reported an attempt they made in the design of expert system for the diagnosis of eye diseases. The system adopted neural network as technique of knowledge representation and decision tree as technique for its reasoning process. The system could also explain rational of diagnosis to the user. Unfortunately, the range and capacity of diagnosis is low as the system uses only 22 signs & symptoms and could diagnosis maximum of 12 eye diseases. NNDTEDD is meant for use by doctors although it is very promising in training young ophthalmologists. However, it is not designed for use by non-medical experts.
In the review made so far, we have attempted to look in details the various techniques and methods used by researchers in the development of expert system for eye disease diagnosis, all of which made diagnosis based only on unverified symptoms presented by the patient. In reality, the process of disease diagnosis by eye doctors does not rely on symptoms alone. The doctor also conducts visual acuity test in order to ascertain the actual way the patient sees and uses this value to validate patient‘s symptom claims as well as guide his diagnosis. The various methods and techniques applied by other researchers in the development of visual acuity measurement system and eye examination protocol is hereby discussed.
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