**1. Introduction**

Clinical Decision Support Systems (CDSS) provide clinicians, staff, patients, and other indi‐ viduals with knowledge and person-specific information, intelligently filtered and present‐ ed at appropriate times, to enhance health and health care [1]. Medical errors have already become the universal matter of international society. In 1999, IOM (American Institute of Medicine) published a report "To err is Human" [2], that indicated: First, the quantity of medical errors is incredible, the medical errors had already became the fifth lethal; Second, the most of medical errors occurred by the human factor which could be avoid via the com‐ puter system. Improving the quality of healthcare, reducing medical errors, and guaranty‐ ing the safety of patients are the most serious duty of the hospital. The clinical guideline can enhance the security and quality of clinical diagnosis and treatment, its importance already obtained widespread approval [3]. In 1990, clinical practice guidelines were defined as "sys‐ tematically developed statements to assist practitioner and patient decisions about appropri‐ ate health care for specific clinical circumstances" [4].

The clinical decision support system (CDSS) is any piece of software that takes as input in‐ formation about a clinical situation and that produces as output inferences that can assist practitioners in their decision making and that would be judged as "intelligent" by the pro‐ gram's users [5].

Artificial intelligence has been successfully applied in medical diagnosis. They have been used for skin disease diagnosis, fetal delivery, metabolic synthesis as demonstrated in [6,7 and 8]. Artificial neural networks are artificial intelligence paradigms; they are machine learning tools which are loosely modelled after biological neural systems. They learn by training from past experience data and make generalization on unseen data. They have been applied as tools for modelling and problem solving in real world applications such as

speech recognition, gesture recognition, financial prediction, and medical diagnostics [9, 10, 11 and 12]. Backpropagation employs gradient descent learning and is the most popular al‐ gorithm used for training neural networks. Neural networks were recently viewed as 'black boxes' as they could not explain how they arrived to a particular solution. Knowledge ex‐ traction is the process of extracting valuable information from trained neural networks in the form of 'if-then' rules as shown in [13 and 14]. The extracted rules describe the knowl‐ edge acquired by neural networks while learning from examples.

perts in understanding which combination of symptom, physical eye examination and pa‐ tient's complain constituents have a major role in the eye problem. The rules contain information for sorting eye diseases according to their symptoms, physical condition and complain from the patient and knowledge acquired by neural networks from training on

Neural Networks and Decision Trees For Eye Diseases Diagnosis

http://dx.doi.org/10.5772/51380

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**2. Application of Neural network in Clinical decision Support System**

These days the Artificial Neural Networks(ANN) have been widely used as tools for solving many decisions modeling problems. The various capabilities and properties of ANN like Non-parametric, Non-linearity, Input-Output mapping, Adaptivity make it a better alterna‐ tive for solving massively parallel distributive structure and complex task in comparison of statistical techniques, where rigid assumptions are made about the model. Artificial Neural Networks being non-parametric, makes no assumption about the distribution of data and thus capable of "letting the data speak for itself". As a result, they are natural choice for modeling complex medical problems where large database of relevant medical information

In biomedicine, the assessment of vital functions of the body often requires noninvasive measurements, processing and analysis of physiological signals. Examples of physiological signals found in biomedicine include the electrical activity of the brain-the electroencephalo‐ gram (EEG), the electrical activity of the heart-the electrocardiogram (ECG), the electrical ac‐ tivity of the eye-i.e. PERG and EOG-respiratory signals, blood pressure and temperature

Often, biomedical data are not well behaved. They vary from person to person, and are af‐ fected by factors such as medication, environmental conditions, age, weight, mental and physical state. Consequently, clinical expertise is often required for a proper analysis and in‐ terpretation of medical data. This has led to the integration of signal processing with intelli‐ gent techniques such as artificial neural networks (ANN), expert systems and fuzzy logic to

ANN has been proposed as a reasoning tool to support clinical decision-making since 1959 [17]. Some problems encountered have led to significant developments in computer science, but it was only during the last decade of the last century that decision support systems have

The literature reports several applications of ANNs to the recognition of a particular pathol‐ ogy. For example, cancer diagnosis [18 and 19], automatic recognition of alertness and drowsiness from electroencephalography [20], predictions of coronary artery stenosis [21], analysis of Doppler shift signals [22 and 23], classify and predict the progression of thyroidassociated ophthalmopathy [24], diabetic retinopathy classification [25], saccade detection in

been routinely used in clinical practice on a significant scale [16].

EOG recordings [26] and PERG classification [22].

previous samples.

are available [15].

signals [16].

improve performance [16].

The human eye is the organ which gives us the sense of sight allowing us to learn more about the surrounding world than we do with any of the other four senses. We use our eyes in almost every activity we perform whether reading, working, watching television, writing a letter, driving a car and in countless other ways. Most people probably would agree that sight is the sense they value more than all the rest.

A recent survey of 1,000 adults shows that nearly half - 47% - worry more about losing their sight than about losing their memory and their ability to walk or hear. But almost 30% indi‐ cated that they don't get their eyes checked. Many Americans are unaware of the warning signs of eye diseases and conditions that could cause damage and blindness if not detected and treated soon enough.

In spite of the high prevalence of vision disorders in this country, so far, few victims receive professional eye care due to one of the following reasons;


Due to all of these, late detection of vision disorders and unnecessary loss of vision is en‐ countered. But with a computer based system (expert system), over dependence on human expert can be minimized. This will go a long way to save time and furthermore early detec‐ tion of eye disease can be adequately addressed. Cost for the services can also be reduced as a lot of unnecessary laboratory test may be avoided with the use of the proposed system.

This study classifies eye diseases using patient complaint, symptoms and physical eye ex‐ aminations. The disease covered includes the following eye disease; Pink eye (conjunctivi‐ tis), Uveitis, Glaucoma, Cataract, Macular Degeneration, retinal detachment, Corneal ulcer, Color blindness, Far sightedness(hyperopia), Near sighteness(myopia), and Astigmatism.

We train artificial neural networks to classify eye diseases according to patient complain, symptoms and physical eye examination. We then use decision trees to extract knowledge from trained neural networks in order to understand the knowledge represented by the trained networks. Finally, we apply decision trees to build a tree structure for classification on the same sets of data sample we used to train neural networks earlier. In this way we combine neural networks and decision trees through training and knowledge extraction. The extracted knowledge from neural networks is transformed as rules which will help ex‐ perts in understanding which combination of symptom, physical eye examination and pa‐ tient's complain constituents have a major role in the eye problem. The rules contain information for sorting eye diseases according to their symptoms, physical condition and complain from the patient and knowledge acquired by neural networks from training on previous samples.

speech recognition, gesture recognition, financial prediction, and medical diagnostics [9, 10, 11 and 12]. Backpropagation employs gradient descent learning and is the most popular al‐ gorithm used for training neural networks. Neural networks were recently viewed as 'black boxes' as they could not explain how they arrived to a particular solution. Knowledge ex‐ traction is the process of extracting valuable information from trained neural networks in the form of 'if-then' rules as shown in [13 and 14]. The extracted rules describe the knowl‐

The human eye is the organ which gives us the sense of sight allowing us to learn more about the surrounding world than we do with any of the other four senses. We use our eyes in almost every activity we perform whether reading, working, watching television, writing a letter, driving a car and in countless other ways. Most people probably would agree that

A recent survey of 1,000 adults shows that nearly half - 47% - worry more about losing their sight than about losing their memory and their ability to walk or hear. But almost 30% indi‐ cated that they don't get their eyes checked. Many Americans are unaware of the warning signs of eye diseases and conditions that could cause damage and blindness if not detected

In spite of the high prevalence of vision disorders in this country, so far, few victims receive

**•** Specialist in eye diseases(ophthalmologist) are few and ophthalmology clinic are also few

**•** Lack of knowledge that early professional eye care is needed when symptoms are sus‐

Due to all of these, late detection of vision disorders and unnecessary loss of vision is en‐ countered. But with a computer based system (expert system), over dependence on human expert can be minimized. This will go a long way to save time and furthermore early detec‐ tion of eye disease can be adequately addressed. Cost for the services can also be reduced as a lot of unnecessary laboratory test may be avoided with the use of the proposed system.

This study classifies eye diseases using patient complaint, symptoms and physical eye ex‐ aminations. The disease covered includes the following eye disease; Pink eye (conjunctivi‐ tis), Uveitis, Glaucoma, Cataract, Macular Degeneration, retinal detachment, Corneal ulcer, Color blindness, Far sightedness(hyperopia), Near sighteness(myopia), and Astigmatism.

We train artificial neural networks to classify eye diseases according to patient complain, symptoms and physical eye examination. We then use decision trees to extract knowledge from trained neural networks in order to understand the knowledge represented by the trained networks. Finally, we apply decision trees to build a tree structure for classification on the same sets of data sample we used to train neural networks earlier. In this way we combine neural networks and decision trees through training and knowledge extraction. The extracted knowledge from neural networks is transformed as rules which will help ex‐

edge acquired by neural networks while learning from examples.

sight is the sense they value more than all the rest.

professional eye care due to one of the following reasons;

**•** Inability to pay for the needed services.

and treated soon enough.

64 Advances in Expert Systems

pected.
