Classification of Cholesterol Levels Using Moment Invariant Feature Extraction and K-Nearest Neighbor (KNN) Algorithm
Cholesterol is a fat that is mostly formed by the body itself, especially in the liver. Cholesterol is very useful for the body but will be very dangerous if it has excessive levels. The impact of excessive cholesterol is the emergence of deadly diseases such as heart disease, stroke and poor blood circulation. In this study, one of the medical sciences that can be used to detect cholesterol levels is Iridology. This iridology itself can be applied in computer science which is often referred to as Digital Image Processing. In this case, the feature recognition method will be used using Moment Invariant feature extraction and the K-Nearest Neighbor Algorithm. Where the data used is the Dataset from Ubiris V1. With the resulting accuracy of 84,8485%.