A computer-implemented method for supporting disease analysis, the method comprising: obtaining an analysis image by capturing an analysis target cell contained in a specimen collected from a subject, wherein the target analysis cell is any cell that is contained in the specimen; converting the analysis image into brightness, first hue and second hue; expressing the brightness, the first hue and the second hue as tone matrices (79y, 79cb, 79cr); generating, on the basis of the tone matrices (79y, 79cb, 79cr), for each pixel, tone vector data by combining gradation values of the brightness, the first hue, and the second hue; generating, as analysis data, a set of the tone vector data; extracting, by a first neural network, a feature quantity of a cell from the analysis data; inputting a result of the first neural network into a first second neural network and into a second second neural network, wherein the first second neural network generates first information regarding a type of abnormal finding, the first information being a probability of a presence or absence of an abnormal finding, and the second second neural network generates second information regarding a type of cell, the second information being a probability that the analysis target cell in the analysis image belongs to each of types of cells; generating, by a machine learning algorithm, an analysis result being a disease name or a label value indicating the disease name, on the basis of the first information and the second information, wherein the first neural network is a convolution connect neural network and the second neural networks (61, 62) are each a full connect neural network. obtaining an analysis image by capturing an analysis target cell contained in a specimen collected from a subject, wherein the target analysis cell is any cell that is contained in the specimen; converting the analysis image into brightness, first hue and second hue; expressing the brightness, the first hue and the second hue as tone matrices (79y, 79cb, 79cr); generating, on the basis of the tone matrices (79y, 79cb, 79cr), for each pixel, tone vector data by combining gradation values of the brightness, the first hue, and the second hue; generating, as analysis data, a set of the tone vector data; extracting, by a first neural network, a feature quantity of a cell from the analysis data; inputting a result of the first neural network into a first second neural network and into a second second neural network, wherein the first second neural network generates first information regarding a type of abnormal finding, the first information being a probability of a presence or absence of an abnormal finding, and the second second neural network generates second information regarding a type of cell, the second information being a probability that the analysis target cell in the analysis image belongs to each of types of cells; the first second neural network generates first information regarding a type of abnormal finding, the first information being a probability of a presence or absence of an abnormal finding, and the second second neural network generates second information regarding a type of cell, the second information being a probability that the analysis target cell in the analysis image belongs to each of types of cells; generating, by a machine learning algorithm, an analysis result being a disease name or a label value indicating the disease name, on the basis of the first information and the second information, wherein the first neural network is a convolution connect neural network and the second neural networks (61, 62) are each a full connect neural network.