HILL CLIMBING ALGORITHM ON BAYESIAN NETWORK TO DETERMINE PROBABILITY VALUE OF SYMPTOMS AND EYE DISEASE
Abstract
One of the five human senses referred to as photoreceptors is the eye because the eye is very sensitive to light stimuli. Refractive abnormalities in the eyes are often experienced, which are abnormalities that occur when the eyes cannot see clearly in the open or blurred vision. An unhealthy lifestyle is a trigger for an increase in individuals who experience complaints of eye diseases. In diagnosing a disease, doctors need patient information in the form of symptoms experienced so that patients can be treated immediately. Information in the form of symptoms and types of eye diseases can be used to make conjectures about eye diseases through the structure of BN. The symptom information and type of the disease are represented through nodes, while the relationships are represented through the edge. BN is one of the Probabilistic Graphical Models (PGM) consisting of nodes and edges. BN is also known as a direct acyclic graph (DAG), which is a directed graph that does not have a cycle. The approach method used is scored based on the evaluation process with the bic scoring function. The algorithm used in this study is the HC algorithm. The research data used consisted of 52 symptoms and 15 eye diseases. The results of the study were obtained by the final structure of BN formed by the HC algorithm produced 93 edges and 65 connected nodes, and the probability value of the disease and the symptoms of the disease in the eye.
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References
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