Abstract
This research aimed to develop a non-invasive method for identifying and detecting cholesterol levels. It has developed this innovative method and a Smart Controller to measure cholesterol levels. This research identified the cholesterol level under image processing based on hand-skin image processing. For the investigation, several sample pictures with and without cholesterol were taken. The kinds of samples are based on age classifications, for example, 20–40 and 40 < 60 years old. The 26 sample pictures were evaluated and investigated under the gray-level co-occurrence matrix (GLCM) for accuracy and simple analysis. This research used an artificial neural network (ANN) to train and test hand texture to detect cholesterol levels. This model performance was assessed using the Root Mean Square Error (RMSE) and correlation coefficient (r). The Clarke Error Grid Analysis (EGA) of variance was employed in this investigation to determine the instrument's accuracy. The results indicated that the RMSE is appropriate as the standard value, r is 0.95, and Clarke EGA analysis: 92% and 88% for both age classifications.
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