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 ; generating, on the basis of the tone matrices , 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 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 ; generating, on the basis of the tone matrices , 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 are each a full connect neural network. <EOS>