Method and diagnostic apparatus for determining enteric disorder using machine learning model

A diagnostic apparatus for diagnosing an enteric disorder by using a machine learning model, comprising:

• a processor;
• and a memory for storing instructions which when executed by the processor cause the processor to:
• extract multiple microbial data based on an analysis result of a mixture of a gut-derived substance collected from a subject and a gut environment-like composition, wherein the gut environment-like composition comprises L-cysteine hydrochloride and mucin, including culturing the mixture under anaerobic conditions for 18 to 24 hours, centrifuging the cultured mixture to separate a supernatant and a precipitate, and analyzing the supernatant and the precipitate to extract data on at least one of a content, a concentration or a kind of one or more of endotoxins, hydrogen sulfides, short-chain fatty acids (SCFAs) or microbiota-derived metabolites contained in the cultured mixture, and a change in kind, a concentration, a content or a diversity of bacteria included in the microbiota;
• select multiple microbe-related features to be used for the machine learning model from the multiple microbial data based on a predetermined feature selection algorithm, wherein the selecting of the multiple microbe-related features comprises:
• (a) performing a primary selection from features through a Boruta algorithm to select a subset of features;
• (b) checking an error value depending on a number of features through a binomial deviance plot to determine an optimal range of the number of features, wherein the optimal range is 1 to 23;
• (c) determining an optimal number of features within the optimal range based on accuracy, wherein the optimal number is 14;
• and
• (d) selecting the 14 multiple microbe-related features through an extreme gradient boosting (XGB) model based on importance of the features;
• train the machine learning model by using the multiple microbe-related features, and to perform supervised learning based on labeling of whether the enteric disorder is present for each of the microbial data and contents of microbes included in the selected multiple microbe-related features;
• and
• diagnose the enteric disorder by inputting, into the trained machine learning model, the microbial data collected from the subject to be tested, wherein diagnosing of the enteric disorder is based on an output value of the trained machine learning model,
• wherein each of the microbes is selected from genera comprising the family Tannerellaceae, the family Bifidobacteriaceae, the family Ruminococcaceae, the family Clostridaceae, the family Lachnospiraceae, the family Bacteroidaceae, the family Erysipelatoclostridiaceae, the family Veilonellaceae, the family Bacteroidaceae, the family Ruminococcaceae, the family Lachnospiraceae, and the family Anaerovoracaceae.