Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method

Abstract

Purpose : Early detection of anemia based on nails and palms images by applying the Naive Bayes method, as well as to measure the level of accuracy in detecting anemia. Design/methodology/approach : Using the Naive Bayes method. System development uses the waterfall method. Findings/result : Based on the results of the tests that have been carried out, the resulting accuracy is 87.5% with varying light intensities and is 92.3% by using a light intensity of 5362 Lux. Originality/value/state of the art : The difference between this study and previous research is in the image pre-processing method and classification method. In this study, the images of the nails and palms were converted to the YCbCr color space to be segmented and color features extracted. Then the color features will be classified using the Naive Bayes classification method. The output of this system is the result of the input image classification, whether normal or anemic.