Human body metabolism system physiological age prediction method based on machine learning
Through the gender-specific physiological age prediction model of the metabolic system, linear regression correction is used to use indicators such as total cholesterol to solve the problem of inaccurate physiological age prediction in the prior art, and the accurate assessment of the physiological age of the metabolic system in men and women is achieved.
Patent Information
- Application Number
- CN202510380206.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-28
- Publication Date
- 2025-07-04
AI Technical Summary
In the prior art, a single machine learning model is difficult to accurately reflect the physiological age of different organs or systems of an individual, and there is a large difference between men and women, resulting in inaccurate prediction of physiological age.
A machine learning-based method was used to model blood detection data of the metabolic system in men and women, and a gender-specific physiological age prediction model was constructed using indicators such as total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, blood sugar, glycated hemoglobin and triglycerides, and the calendar age was corrected through linear regression to improve prediction accuracy.
Accurate assessment of the physiological age of individual metabolic systems in different genders is achieved, which reduces prediction errors and improves the accuracy of physiological age prediction and personalized evaluation ability.
Smart Images
Figure CN120260920A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of machine learning, and particularly relates to a method for predicting the physiological age of the human metabolic system based on machine learning with good prediction effect. Background Art
[0002] The metabolic system refers to a series of complex chemical reactions and regulatory mechanisms in the body for maintaining the energy and material balance required for life activities. It includes two major aspects: energy metabolism and material metabolism, mainly achieving energy conversion and material circulation through the decomposition and synthesis of carbohydrates, fats, and proteins. The metabolic system involves multiple organs and tissues, including the liver, pancreas, muscles, adipose tissue, etc., and works in coordination under the regulation of hormones (such as insulin and glucagon). The main functions of the metabolic system are to provide energy (such as ATP), synthesize biomolecules (such as hormones and enzymes), and remove waste in the body (such as urea and lactic acid). Metabolic disorders may lead to metabolic diseases such as diabetes, obesity, and hyperlipidemia.
[0003] Total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c), and triglyceride (TG) are key biochemical indicators in the metabolic system. Elevated levels of CHOL and LDL are closely related to atherosclerosis and cardiovascular diseases, while HDL has a protective effect against atherosclerosis. GLU and HbA1c are important indicators of glucose metabolism, reflecting the immediate blood glucose level and long-term blood glucose control status respectively, and abnormal levels indicate the risk of diabetes and metabolic disorders. TG is an important indicator of lipid metabolism, and its elevation is related to insulin resistance, obesity, and metabolic syndrome. These indicators usually act together to indicate the state of the metabolic system, and abnormalities in these indicators usually predict the risk of cardiovascular diseases and other metabolic diseases, which need to be managed through lifestyle intervention and drug treatment.
[0004] Calendar age refers to the age calculated according to time after birth and is an objective and fixed number. Physiological age, on the other hand, reflects the actual physiological state and functional level of the body and is affected by various factors such as lifestyle, health status, and genetics. Physiological age may be higher or lower than calendar age. By maintaining good living habits, appropriate exercise, and a balanced diet, the physiological age can be effectively delayed and the physical health level can be improved.
[0005] Metabolic system age is a quantitative indicator based on relevant blood test metrics that can assess the physiological aging degree of an individual's metabolic system. It reflects the actual functional age of the metabolic system and can be used to measure the health status and aging rate of the metabolic system. Referring to the article published in "Nature Medicine" in 2023 by Tian, Y.E, etc., the physiological ages of various organs and systems of disease patients are significantly higher than those of healthy individuals, and bad living habits (such as smoking, drinking, staying up late, etc.) are also associated with higher physiological ages.
[0006] Most of the existing physiological age assessment schemes use all physical examination indicators to construct machine learning models, without classifying relevant indicators into corresponding organs or systems. However, different organs or systems of an individual often have different physiological age indices, and a single overall model is difficult to accurately reflect the physiological ages of different organs or systems of an individual. Moreover, some indicators vary greatly between men and women, and a single model is difficult to reflect the physiological ages of different organs or systems of individuals of different genders.
[0007] Therefore, it is necessary to propose an improvement to overcome the defects of the existing technology. Summary of the Invention
[0008] The object of the present invention is to solve the problems in the existing technology and provide a machine learning-based method for predicting the physiological age of the human metabolic system with good prediction effect.
[0009] The technical solution of the present invention is: A method for predicting the physiological age of the human metabolic system based on machine learning, comprising the following steps: S1. Collect physical examination data of the human metabolic system: The physical examination data of the human metabolic system includes the gender of the person being examined, the calendar age, and the blood test data of the metabolic system; The blood test data of the metabolic system includes: total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c), and triglyceride (TG); S2. Divide the physical examination data of the human metabolic system into a male data set and a female data set according to gender; S3. Divide the male data set and the female data set into a training set and a test set according to a ratio respectively; S4. Perform normalization processing on the data of the training set: Perform normalization processing on the data in the training sets of the male data set and the female data set respectively; S5. Build a model and perform model training: Build models for the data in the training sets of the male data set and the female data set respectively, and perform cross-validation hyperparameter optimization by means of grid search; S6. Perform prediction and correction of the physiological age of the human metabolic system: Perform age correction according to the regression to the mean effect, use the calendar age as the independent variable, and the original age difference as the dependent variable for linear regression; Original age difference = slope * calendar age + intercept + residual; Among them, the residual is the age difference after correction, that is, the part related to the calendar age is removed from the original age difference, and the formula is: Corrected age difference = original age difference – (slope * original age difference + intercept); Among them, the slope and the intercept are calculated, and the specific calculation method is: Perform linear fitting on the original age difference and the calendar age in the training sets of the male data set and the female data set respectively to obtain the slope and the intercept in the male data set and the female data set.
[0010] As a preferred technical solution, after step S6, the following steps are further included: S7. Calculate the aging index: The formula for calculating the aging index is: Aging index = corrected age difference / calendar age.
[0011] As a preferred technical solution, the ratio of the data volume of the training set to the test set in step S3 is 2:1, 3:1, 4:1 or 5:1.
[0012] As a preferred technical solution, in the normalization processing of the data of the training set in step S4, the normalization method can be any one of Z-score standardization, maximum-minimum normalization, median normalization, and maximum absolute value normalization.
[0013] As a further preferred technical solution, in step S4 for normalizing the data of the training set, the normalization method is min-max normalization, x′ = (x − min(x)) / (max(x) − min(x)), where: x is the original value, min(x) is the minimum value in the data set, max(x) is the maximum value in the data set, and x′ is the value after correction.
[0014] As a preferred technical solution, in step S5, the following one or more regression machine learning models are used for modeling: SVR, LightGBM, CatBoost, Ridge, RandomForest, XGBoost, Huber, Lasso, ElasticNet.
[0015] As a further preferred technical solution, in step S5, SVR modeling is used; in step S5, the data of the training sets in the male data set and the female data set are respectively modeled, and the hyperparameter optimization by cross-validation is performed by grid search, which specifically includes the following steps: S51, create a model; S52, define a parameter grid: the parameter grid includes a penalty coefficient, a kernel function, a kernel function coefficient, and a slack variable; S53, perform hyperparameter tuning; S54, obtain the optimal parameters and the model.
[0016] As a further preferred technical solution, after step S54, the following steps are further included: S55, perform evaluation and verification on the training set and / or the test set.
[0017] As another further preferred technical solution, the hyperparameter tuning in step S53 is specifically performed using GridSearchCV for hyperparameter tuning.
[0018] A method for predicting the physiological age of the human metabolic system based on machine learning collects six blood test indicators closely related to the metabolic system, namely total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c), and triglyceride (TG). Computer models are built for male and female data respectively around the physical examination data of the metabolic system to construct a computer model for predicting the physiological age of the human metabolic system of both genders, so as to evaluate the physiological age of the human metabolic system of individuals of different genders. In a method for predicting the physiological age of the human metabolic system based on machine learning of the present invention, since only the blood data indicators of total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c), and triglyceride (TG) related to the human metabolic system are used as the core data for modeling to construct a physiological age model of the human metabolic system, the physiological age of the human metabolic system can be evaluated specifically; moreover, in a method for predicting the physiological age of the human metabolic system based on machine learning of the present invention, modeling is carried out separately for men and women based on the data differences between genders, making the prediction results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 is a flowchart of the specific implementation manner of a method for predicting the physiological age of the human metabolic system based on machine learning of the present invention; Figure 2 is the prediction result of the physiological age model of the male metabolic system in the test set in a method for predicting the physiological age of the human metabolic system based on machine learning of this embodiment; Figure 3 is the prediction result of the physiological age model of the female metabolic system in the test set in a method for predicting the physiological age of the human metabolic system based on machine learning of this embodiment; Figure 4 is the male age difference after age prediction correction through step S6 in a method for predicting the physiological age of the human metabolic system based on machine learning of this embodiment; Figure 5 is the female age difference after age prediction correction through step S6 in a method for predicting the physiological age of the human metabolic system based on machine learning of this embodiment. SPECIFIC IMPLEMENTATION MANNER
[0020] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0021] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. "Plural" generally includes at least two, but does not exclude the case of including at least one.
[0022] It should be understood that the term "and / or" used herein is only an associative relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.
[0023] Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "when...", "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" may be interpreted as "when determined", "in response to determining", "when detecting (stated condition or event)", or "in response to detecting (stated condition or event)".
[0024] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such commodity or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the commodity or system including the said element.
[0025] As Figure 1 shown is a specific implementation manner of a method for predicting the physiological age of a human metabolic system based on machine learning according to the present invention. A method for predicting the physiological age of a human metabolic system based on machine learning in this embodiment includes the following steps: S1. Collect physical examination data of the human metabolic system: The physical examination data of the human metabolic system includes the gender of the examinee, the calendar age, and the blood test data of the metabolic system; the blood test data of the metabolic system includes: total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c), and triglyceride (TG). S2. Divide the physical examination data of the human metabolic system into a male data set and a female data set according to gender. S3. Divide the male data set and the female data set into a training set and a test set according to a certain proportion respectively. S4. Perform normalization processing on the data of the training set: Perform normalization processing on the data in the training sets of the male data set and the female data set respectively. S5. Build a model and perform model training: Build models for the data in the training sets of the male data set and the female data set respectively, and perform cross-validation hyperparameter optimization in the way of grid search. S6. Perform prediction and correction of the physiological age of the human metabolic system: Perform age correction according to the regression to the mean effect, use the calendar age as the independent variable, and the original age difference as the dependent variable for linear regression. Original age difference = slope * calendar age + intercept + residual. Among them, the residual is the age difference after correction, that is, the part related to the calendar age is removed from the original age difference. The formula is: Corrected age difference = original age difference – (slope * original age difference + intercept). Among them, the slope and the intercept are calculated. The specific calculation method is: Perform linear fitting on the original age difference and the calendar age in the training sets of the male data set and the female data set respectively to obtain the slope and the intercept in the male data set and the female data set.
[0026] In this embodiment, after step S6, the following steps are further included: S7. Calculate the aging index: The calculation formula of the aging index is: Aging index = corrected age difference / calendar age.
[0027] Specifically, in a method for predicting the physiological age of the human metabolic system based on machine learning in this implementation, 862 male individual samples were collected, and the age distribution range was 15 - 90 years old; 721 female individual samples were collected, and the age distribution range was 17 - 88 years old.
[0028] In step S3, the ratio of the data volume of the training set to the test set is 3:1. After division: among male individuals, the number of training set samples is 647, and the number of test set samples is 215; among female individuals, the number of training set samples is 541, and the number of test set samples is 180. It should be noted that in practical applications, the ratio of the training set to the test set can be 4:1, 3:1, 2:1 or 5:1 according to needs, which does not affect the implementation of the solution of the present invention.
[0029] In practical applications, in step S4 for normalizing the data of the training set, the normalization method can be any one of Z-score standardization, maximum-minimum normalization, median normalization, and maximum absolute value normalization. In a method for predicting physiological age of the human body metabolism system based on machine learning in this embodiment, in step S4 for normalizing the data of the training set, the normalization method selects maximum-minimum normalization, x′ = (x−min(x)) / (max(x)−min(x)), where: x is the original value, min(x) is the minimum value in the data set, max(x) is the maximum value in the data set, and x′ is the corrected value.
[0030] In a method for predicting physiological age of the human body metabolism system based on machine learning of the present invention, in specific applications, in step S5, the following one or more regression machine learning models can be used for modeling: SVR, LightGBM, CatBoost, Ridge, RandomForest, XGBoost, Huber, Lasso, ElasticNet. In a method for predicting physiological age of the human body metabolism system based on machine learning in this embodiment, step S5 uses SVR to build a regression machine learning model. In a method for predicting physiological age of the human body metabolism system based on machine learning in this embodiment, in step S5, the data of the training set in the male data set and the female data set are respectively modeled, and the hyperparameter optimization by cross-validation in the form of grid search specifically includes the following steps: S51. Create a model; S52. Define a parameter grid: The parameter grid includes a penalty coefficient, a kernel function, a kernel function coefficient, and a slack variable; S53. Perform hyperparameter tuning; S54. Obtain the best parameters and model.
[0031] A method for predicting the physiological age of the human metabolic system based on machine learning, in practical applications, after step S54, the following steps are further included: S55. Evaluate and verify on the training set and / or test set. In a method for predicting the physiological age of the human metabolic system based on machine learning in this embodiment, step S55 is to evaluate and verify on both the training set and the test set. Moreover, in this embodiment, the specific implementation of hyperparameter tuning in step S53 is to use GridSearchCV for hyperparameter tuning.
[0032] The specific implementation manner of step S5 of a method for predicting the physiological age of the human metabolic system based on machine learning in this embodiment using female data, maximum-minimum normalization, and SVR modeling in python is as follows: Import the necessary python packages: import numpy as np import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler, MinMaxScaler from sklearn.svm import SVR from sklearn.metrics import mean_squared_error from scipy.stats import pearsonr # Read in the modeling data: data = pd.read_csv(“Metabolic System Modeling Data.csv”, header =0, index_col = 0) # Obtain female data femaledf = data[data[“gender”]==”female”] # Obtain the detection value matrix of 6 indicators: X = femaledf[["CHOL", "LDL","HDL", "TG","GLU", “HbA1c”]] # Obtain the age y = femaledf["age"] # Split the training set and test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) # Create a modeling pipeline: pipeline = Pipeline( ("scaler", MinMaxScaler()), ("svm", SVR()) ) # Define the parameter grid: param_grid = { "svm__C": [0.1, 1, 10, 100], # Penalty coefficient "svm__kernel": ["linear", "rbf"], # Kernel function "svm__gamma": ["scale", "auto"], # Kernel coefficient "svm__epsilon": [0.1, 0.2, 0.5], # Slack variable } # Use GridSearchCV for hyperparameter tuning: grid_search = GridSearchCV( estimator=pipeline, param_grid=param_grid, cv=5, scoring="neg_mean_squared_error", verbose=1 ) grid_search.fit(X_train, y_train) # Get the best parameters and model: best_params = grid_search.best_params_ best_model = grid_search.best_estimator_ # Evaluate on the training set y_train_pred = best_model.predict(X_train) train_mae = mean_squared_error(y_train, y_train_pred) train_r, train_p = pearsonr(y_train, y_train_pred) # Evaluate on the test set: y_test_pred = best_model.predict(X_test) test_mae = mean_squared_error(y_test, y_test_pred) test_r, test_p = pearsonr(y_test, y_test_pred) In a method for predicting physiological age of the human metabolic system based on machine learning according to this embodiment, the slope and intercept in step S6 are calculated. The specific calculation method is as follows: The raw age difference and calendar age are linearly fitted in the training sets of the male dataset and the female dataset respectively to obtain the slope and intercept in the male dataset and the female dataset. In this embodiment, when the normalization method is selected as min-max normalization and the SVR is used for modeling, by linearly fitting the raw age difference and calendar age in the training sets of the male dataset and the female dataset, the slope and intercept in the male dataset and the female dataset are obtained as follows: the male slope is -0.8804, the male intercept is 47.01, the female slope is -0.8600, and the female intercept is 43.23.
[0033] A method for predicting physiological age of the human metabolic system based on machine learning according to this embodiment has achieved good performance in practical applications. In the test set, as Figure 2 shown, the Pearson correlation coefficient between the physiological age predicted by the male metabolic system physiological age model and the calendar age has reached 0.21. As Figure 3 shown, the correlation coefficient between the female physiological age predicted by the female metabolic system physiological age model and the calendar age has reached 0.39, both showing relatively good modeling effects. However, both models have the "regression to the mean effect" in statistics, that is, for young individuals, the predicted biological age is often higher than the calendar age; for older individuals, the predicted biological age is often lower than the calendar age. After age prediction correction in step S6, the age differences after correction for males and females are respectively as Figure 4 and Figure 5As shown, the MAE (Mean Absolute Error) after male correction is 3.00, and the MAE after female correction is 2.90, achieving a very good result.
[0034] A method for predicting the physiological age of the human metabolic system based on machine learning in this embodiment also defines and calculates the aging index through step S7. The aging index measures the degree of change of the physiological age relative to the calendar age and can evaluate the aging rate. If the aging index is greater than 0, it means that the individual's aging speed is higher than the normal speed and the aging speed is faster; if the aging index is less than 0, it represents that the individual's aging speed is slower than the normal speed and the aging speed is slower.
[0035] A method for predicting the physiological age of the human metabolic system based on machine learning of the present invention collects 6 blood test indexes closely related to the metabolic system, namely total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c) and triglyceride (TG). Computer model modeling is carried out for male and female data respectively around the physical examination data of the metabolic system, and a computer model for predicting the physiological age of the male and female human metabolic systems is constructed to evaluate the physiological age of individuals of different genders in the human metabolic system. A method for predicting the physiological age of the human metabolic system based on machine learning of the present invention, because only the blood data indexes of total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c) and triglyceride (TG) related to the human metabolic system are used as the core data for modeling to construct the physiological age model of the human metabolic system, the physiological age of the human metabolic system can be evaluated specifically; moreover, a method for predicting the physiological age of the human metabolic system based on machine learning of the present invention models for men and women respectively based on the data differences between men and women, making the prediction results more accurate.
[0036] In summary, the above are only the preferred embodiments of the present invention and are not used to limit the scope of implementation of the present invention. That is, all equivalent changes and modifications made according to the content of the patent application scope of the present invention shall fall within the technical scope of the present invention.
Claims
1. A method for predicting the physiological age of the human metabolic system based on machine learning, characterized in that: It includes the following steps: S1. Collect physical examination data of the human metabolic system: The physical examination data of the human metabolic system includes the gender of the person being examined, the calendar age, and the blood test data of the metabolic system; The blood test data of the metabolic system includes: total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), blood glucose (GLU), glycated hemoglobin (HbA1c), and triglyceride (TG); S2. Divide the physical examination data of the human metabolic system into a male data set and a female data set according to gender; S3. Divide the male data set and the female data set into a training set and a test set according to a certain proportion respectively; S4. Perform normalization processing on the data of the training set: Perform normalization processing on the data in the training sets of the male data set and the female data set respectively; S5. Build a model and perform model training: Build models for the data in the training sets of the male data set and the female data set respectively, and perform cross-validation hyperparameter optimization in the way of grid search; S6. Perform prediction and correction of the physiological age of the human metabolic system: Perform age correction according to the regression to the mean effect, use the calendar age as the independent variable, and the original age difference as the dependent variable for linear regression; Original age difference = slope * calendar age + intercept + residual; Among them, the residual is the age difference after correction, that is, the part related to the calendar age is removed from the original age difference. The formula is: Corrected age difference = original age difference – (slope * original age difference + intercept); Among them, the slope and intercept are calculated. The specific calculation method is: Perform linear fitting of the original age difference and the calendar age in the training sets of the male data set and the female data set respectively to obtain the slope and intercept in the male data set and the female data set.
2. The physiological age prediction method of a human metabolic system based on machine learning according to claim 1, characterized in that: After step S6, the following steps are also included: S7. Calculate the aging index: The formula for calculating the aging index is: Aging index = corrected age difference / calendar age.
3. A physiological age prediction method for the human metabolic system based on machine learning according to claim 1, characterized in that: In step S3, the ratio of the data volume of the training set to the test set is 2:1, 3:1, 4:1, or 5:
1.
4. A method for predicting the physiological age of a human metabolic system based on machine learning according to claim 1, characterized in that: In step S4, when performing normalization processing on the data of the training set, the normalization method can be any one of Z-score standardization, maximum-minimum normalization, median normalization, and maximum absolute value normalization.
5. A method for predicting the physiological age of a human metabolic system based on machine learning according to claim 4, characterized in that: In step S4, when performing normalization processing on the data of the training set, the normalization method is maximum-minimum normalization, x′=(x−min(x)) / (max(x)−min(x)), where: x is the original value, min(x) is the minimum value in the data set, max(x) is the maximum value in the data set, and x′ is the value after correction.
6. A method for predicting the physiological age of a human metabolic system based on machine learning according to claim 1, characterized in that: In step S5, the following one or more regression machine learning models are used for modeling: SVR, LightGBM, CatBoost, Ridge, RandomForest, XGBoost, Huber, Lasso, ElasticNet.
7. A method for predicting the physiological age of a human metabolic system based on machine learning according to claim 6, characterized in that: In step S5, SVR modeling is used; in step S5, models are built for the data in the training sets of the male data set and the female data set respectively, and cross-validation hyperparameter optimization is performed in the way of grid search. Specifically, it includes the following steps: S51. Create a model; S52. Define a parameter grid: The parameter grid includes a penalty coefficient, a kernel function, a kernel function coefficient, and a slack variable; S53. Perform hyperparameter tuning; S54. Obtain the optimal parameters and model.
8. A method for predicting the physiological age of a human metabolic system based on machine learning according to claim 7, characterized in that: After step S54, the following steps are further included: S55. Conduct evaluation and verification on the training set and / or the test set.
9. A method for predicting the physiological age of a human metabolic system based on machine learning according to claim 7, characterized in that: The specific implementation of step S53 for hyperparameter tuning is to use GridSearchCV for hyperparameter tuning.