A prediction method for an obesity risk prediction device based on sequencing and data analysis

Abstract

The invention discloses an obesity risk prediction device based on sequencing and data analysis and a prediction method thereof, comprising the following steps: processing original SNP sample data; creating a preliminary model, including classifying obesity and performing regression analysis on height and weight ; create a site scoring model, the site scoring model includes Logistic Regression model, SVM model, Decision Tree model and Random Forest model; use the data in the database to optimize the model; analyze the optimized model; introduce the obesity prediction of integrated learning Model. The invention combines machine learning and gene analysis, utilizes a huge database of genotypes and physiological information of corresponding individuals, and realizes the prediction of individual obesity risk and the assessment of abnormal risk of physiological information.

Description

technical field [0001] The invention belongs to the technical field of gene sequencing, and in particular relates to a prediction method of an obesity risk prediction device based on sequencing and data analysis. Background technique [0002] Existing obesity assessment models (body mass index, waist-to-hip ratio) only include a few physical indicators into the evaluation system, which has great defects. In small-sample studies or individual applications, since the BMI index does not take into account the factors affecting body composition and obesity, its accuracy must be problematic. In addition, the BMI index model ignores the physical differences between men and women, and the physical differences between men and women are ignored in this model, which will lead to misjudgment in obesity prediction due to gender issues. This type of misjudgment is especially common in women. According to relevant studies, there are obvious gender differences in waist circumference and w...

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