Abstract: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.

Abstract: An image compression method is provided. First, an image including a plurality of blocks is received and each block includes a plurality of pixels. Next, an order dithering array within a preset range is generated, which includes a plurality of elements. Then, values of the elements in the order dithering array are respectively mapped to a plurality of mapping values according to a gray scale range of each block. In each block, each pixel value is quantized to a first digital value or a second digital value according to a result of comparing each pixel value with the corresponding mapping value. Hence, a bit rate of the image is efficiently compressed, and features of the original image are preserved inside the compressed image.