Method and diagnostic apparatus for determining constipation using machine learning model

A method for diagnosing the presence or absence of constipation is present by using a machine learning model, comprising:

• a process of analyzing a mixture of a gut-derived substance collected from a subject and a gut environment-like composition;
• a process of extracting multiple microbial data based on an analysis result of the mixture;
• a process of selecting microbe-related features to be used in the machine learning model from the multiple microbial data based on a predetermined feature selection algorithm;
• a process of training the machine learning model with the microbe-related features;
• and
• a process of inputting, to the trained machine learning model, the microbial data collected from the subject to be tested and determining whether constipation is present,
• wherein the microbe-related features include the amount of one or more microbes selected from genera included in families, Lachnospiraceae, Erysipelatoclostridiaceae, Pseudomondaeae, Prevotellaceae, Desulfovibrionaceae, Clostridiaceae, Gemellaceae, Bacteroidaceae, Streptococcaceae, Anaerofustaceae, Monoglobaceae, and RF39.