Implementation of Data Mining Using the K-means Clustering Method to Determine Types of Eye Disease at Wuluhan Health Center
Keywords:
data mining, K-Means, eye diseases, clusterAbstract
The eyes are one of the most important human senses and have received world attention. All human activities which are basically based on receiving visual information require special attention. Data regarding visual impairment throughout the world is based on WHO estimates. Eye health is an important aspect in human health. Eye vision problems such as: glaucoma, cataracts, pseudochaphia, conjunctivitis, macular degeneration, diabetic retinopathy etc. are serious problems that will affect an individual's quality of life. Located at the Wuluhan Community Health Center with patient complaint attributes (anamneses) and eye examinations carried out at the Wuluhan Community Health Center eye clinic. From the results of this assessment, types of eye diseases were grouped, namely glaucoma, conjunctivitis and cataracts. The algorithm used is the K-Means Clustering algorithm and there are 6 attributes for each type of disease. The eye disease data used was 110 patient data, the data testing results were obtained using a 2 cluster to 4 cluster scenario and calculating the Devies-Bouldin Index (DBI) value. The K-Means algorithm calculation for data grouping was carried out for each cluster. For the type of glaucoma disease, the best cluster was in cluster 3 with a DBI value of 1.3232. In terms of conjunctivitis, the best cluster is in cluster 4 with a DBI value of 1.3901. For this type of cataract disease, the best cluster is in cluster 3 with a DBI value of 0.51249
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