Human musculoskeletal system physiological age prediction method based on machine learning

Abstract

According to the human body musculoskeletal system physiological age prediction method based on machine learning, 11 blood detection indexes closely related to a musculoskeletal system are collected in a targeted manner, and computer model modeling is performed on male and female data around musculoskeletal system health data; and constructing a computer model for predicting the physiological age of the human musculoskeletal systems of men and women to evaluate the physiological age of the human musculoskeletal systems of individuals with different genders. According to the human body musculoskeletal system physiological age prediction method based on machine learning, core data only uses data indexes related to a human body musculoskeletal system for modeling, so that the physiological age of the human body musculoskeletal system can be evaluated in a targeted manner; moreover, according to the human body musculoskeletal system physiological age prediction method based on machine learning, modeling is carried out on the male and the female based on the data difference of the male and the female genders, so that the prediction result is more accurate.

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 musculoskeletal system based on machine learning with good prediction effect. Background Art

[0002] The musculoskeletal system is an important part of the human body, consisting of bones, joints and muscles, which jointly support the body structure, protect internal organs, enable movement and maintain postural stability. Bones not only provide a framework for the body, but also participate in mineral storage and hematopoiesis. Muscles enable various body movements, including walking, running, grasping, etc., through contraction and relaxation. The health of the musculoskeletal system is crucial for overall health, affecting an individual's mobility, balance and pain condition.

[0003] The musculoskeletal system is closely related to body composition detection. Body composition detection mainly considers key factors such as muscle mass and fat content to reflect the state of the musculoskeletal system. As the direct executor of movement, the amount of muscle is directly related to the strength and metabolic level of the body; while the fat content also affects the load and function of the musculoskeletal system. By means of body composition detection, we can clearly understand the health of the musculoskeletal system, and then customize scientific exercise and nutrition plans accordingly, prevent adverse conditions such as muscle atrophy and obesity, effectively ensure the efficient operation of body functions, and lay a solid foundation for maintaining overall health.

[0004] Chronological age refers to the age calculated based on time from birth to the present, which is fixed and objective. 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 not be consistent with chronological age, and it may be higher or lower than chronological age. By maintaining a healthy lifestyle, scientific exercise, and a balanced diet, it can effectively help reduce physiological age and make the body more youthful.

[0005] Musculoskeletal age is a quantitative index for evaluating the physiological aging degree of an individual's musculoskeletal system based on relevant indicators of body composition detection. It reflects the actual functional age of the musculoskeletal system and can be used to measure the health status and aging speed of the musculoskeletal system. An article published by Tian, Y.E et al. in "Nature Medicine" in 2023 shows that the physiological ages of various organs and systems of disease patients are significantly higher than those of healthy individuals, and bad lifestyle habits (such as smoking, drinking, staying up late, etc.) are also associated with higher physiological age.

[0006] Most of the physiological age assessment schemes in the prior art use all physical examination indicators to construct machine learning models, and do not divide relevant indicators into corresponding organs or systems. However, different organs or systems of an individual often have different physiological age indices. A single overall model is difficult to accurately reflect the physiological ages of different organs or systems of an individual, and some indicators vary greatly between men and women. A single model is difficult to reflect the physiological ages of different gender individuals and different organs or systems.

[0007] Therefore, it is necessary to propose an improvement to overcome the defects of the prior art. Summary of the Invention

[0008] The object of the present invention is to solve the problems in the prior art and provide a machine learning-based physiological age prediction method for the human musculoskeletal system with good prediction effect.

[0009] The technical solution of the present invention is as follows: A machine learning-based physiological age prediction method for the human musculoskeletal system, comprising the following steps: S1, collecting physical examination data of the human musculoskeletal system: the physical examination data of the human musculoskeletal system includes the gender of the person being examined, the calendar age, and the blood test data of the musculoskeletal system; the blood test data of the musculoskeletal system includes: height, weight, body mass index (BMI), protein content, total body water, inorganic salt content, body fat content, fat-free body weight, skeletal muscle content, basal metabolic rate, and waist-hip ratio; S2, dividing the health examination data into a male data set and a female data set according to gender; S3, respectively dividing the male data set and the female data set into a training set and a test set according to a ratio; S4, performing normalization processing on the data of the training set: respectively performing normalization processing on the data in the training sets of the male data set and the female data set; S5, modeling and performing model training: respectively modeling the data in the training sets of the male data set and the female data set, and performing cross-validation hyperparameter optimization in a grid search manner; S6, performing physiological age prediction correction for the human musculoskeletal system: performing age correction according to the regression to the mean effect, taking 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; where the residual is the age difference after correction, that is, removing the part related to the calendar age from the original age difference, and the formula is: corrected age difference = original age difference – (slope * original age difference + intercept); where the slope and the intercept are calculated, and the specific calculation method is: respectively performing 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 to obtain the slope and the intercept in the male data set and the female data set.

[0010] As a preferred technical solution, the following steps are further included after step S6: 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 process 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 the normalization process of the data of the training set in step S4, the normalization method is maximum-minimum normalization, and the formula is x′ = (x − min(x)) / (max(x) − min(x)), where: x is the original value, min(x) is the minimum value in the dataset, max(x) is the maximum value in the dataset, 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 for modeling; in step S5, the data of the training sets in the male dataset and the female dataset 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 optimal parameters and the model.

[0016] As a further preferred technical solution, the following steps are further included after step S54: 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 to use GridSearchCV for hyperparameter tuning.

[0018] A method for predicting the physiological age of the human musculoskeletal system based on machine learning. By specifically collecting 11 blood test indicators closely related to the musculoskeletal system, namely height, weight, body mass index (BMI), protein content, total body water, inorganic salt content, body fat content, fat-free mass, skeletal muscle content, basal metabolic rate, and waist-hip ratio. Computer models are built for male and female data respectively around the health data of the musculoskeletal system to construct a computer model for predicting the physiological age of the male and female human musculoskeletal systems, so as to evaluate the physiological age of individuals of different genders in the human musculoskeletal system. A method for predicting the physiological age of the human musculoskeletal system based on machine learning in the present invention. Since only the data indicators related to the human musculoskeletal system, such as height, weight, body mass index (BMI), protein content, total body water, inorganic salt content, body fat content, fat-free mass, skeletal muscle content, basal metabolic rate, and waist-hip ratio, are used as the core data for modeling to construct a physiological age model of the human musculoskeletal system, it can specifically evaluate the physiological age of the human musculoskeletal system. And, a method for predicting the physiological age of the human musculoskeletal system based on machine learning in the present invention models for men and women respectively 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 method for a method for predicting the physiological age of the human musculoskeletal system based on machine learning in the present invention; Figure 2 is the prediction result of the male musculoskeletal system physiological age model in the test set in a method for predicting the physiological age of the human musculoskeletal system based on machine learning in this embodiment; Figure 3 is the prediction result of the female musculoskeletal system physiological age model in the test set in a method for predicting the physiological age of the human musculoskeletal system based on machine learning in 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 musculoskeletal system based on machine learning in 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 musculoskeletal system based on machine learning in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[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", "the", and "said" 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 a description of the association relationship of 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 represents an "or" relationship between the associated objects before and after.

[0023] Depending on the context, the words "if" and "when" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detected (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 comprising 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 "comprising one..." does not exclude the existence of another identical element in the commodity or system comprising the said element.

[0025] As Figure 1 shown is a specific implementation manner of a method for predicting the physiological age of the human musculoskeletal system based on machine learning according to the present invention. A method for predicting the physiological age of the human musculoskeletal system based on machine learning in this embodiment includes the following steps: S1. Collect physical examination data of the human musculoskeletal system: The physical examination data of the human musculoskeletal system includes the gender of the examinee, calendar age, and musculoskeletal blood test data; the musculoskeletal blood test data includes: height, weight, body mass index (BMI), protein content, total body water, inorganic salt content, body fat content, fat-free mass, skeletal muscle content, basal metabolic rate, and waist-to-hip ratio; S2. Divide the health examination data 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 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 by means of grid search; S6. Perform prediction and correction of the physiological age of the human musculoskeletal 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 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 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 formula for calculating the aging index is: Aging index = corrected age difference / calendar age.

[0027] Specifically, in a method for predicting the physiological age of the human musculoskeletal system based on machine learning in this implementation, 487 male individual samples were collected, and the age distribution range was 20 - 77 years old; 724 female individual samples were collected, and the age distribution range was 16 - 77 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 325, and the number of test set samples is 162; among female individuals, the number of training set samples is 543, and the number of test set samples is 181. 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 the physiological age of the human musculoskeletal system based on machine learning in this embodiment, in step S4 for normalizing the data of the training set, the maximum-minimum normalization method is selected, and the formula is x′ = (x−min(x)) / (max(x)−min(x)), where: x is the original value, min(x) is the minimum value in the dataset, max(x) is the maximum value in the dataset, and x′ is the value after correction.

[0030] In a method for predicting the physiological age of the human musculoskeletal 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 the physiological age of the human musculoskeletal system based on machine learning in this embodiment, in step S5, SVR is used to build a regression machine learning model. In a method for predicting the physiological age of the human musculoskeletal system based on machine learning in this embodiment, in step S5, the data of the training set in the male dataset and the female dataset are respectively modeled, and hyperparameter optimization is performed through cross-validation in the form of 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 best parameters and model.

[0031] A method for predicting the physiological age of the human musculoskeletal 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 musculoskeletal 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 musculoskeletal system based on machine learning in this embodiment using male data, min-max 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 StandardScale, 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(“Musculoskeletal System Modeling Data.csv”, header =0, index_col= 0) # Obtain male data maledf = data[data[“gender”]==”male”] # Obtain the matrix of detection values for 11 indicators: X = maledf[["WT", "HT", "TBW", "PROTEIN", "MINERAL", "FFM", "SMM", "BMI", "PBF", "BMR", "WHR"]] # Obtain age y = maledf["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=4, scoring="neg_mean_squared_error", verbose=1 ) grid_search.fit(X_train, y_train) # Obtain 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) For a method for predicting the physiological age of the human musculoskeletal system based on machine learning in this embodiment, the slope and intercept in step S6 are calculated. The specific calculation method is as follows: The original 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 SVR is used for modeling, by linearly fitting the original 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 -0.8791, the male intercept is 44.05, the female slope is -0.9689, and the female intercept is 49.54.

[0033] A method for predicting the physiological age of the human musculoskeletal system based on machine learning in 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 cardiovascular physiological age model and the calendar age has reached 0.35. As Figure 3As shown, the correlation coefficient between the physiological age of women predicted by the female cardiovascular physiological age model and the calendar age reached 0.19, both showing good modeling effects. However, both models exhibited the "regression to the mean effect" in statistics, that is, for young individuals, the predicted biological age was often higher than the calendar age; for older individuals, the predicted biological age was often lower than the calendar age. After age prediction correction in step S6, the age differences between men and women after correction are respectively as Figure 4 and Figure 5 shown. The MAE (Mean Absolute Error) of men after correction is 2.88, and the MAE of women after correction is 1.15, achieving very good results.

[0034] A method for predicting the physiological age of the human musculoskeletal system based on machine learning in this embodiment also defines and calculates the aging index through step S7. The aging index represents the number of years an individual ages each year and can evaluate the aging rate. If the aging index is greater than 1, it indicates 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 1, 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 musculoskeletal system based on machine learning of the present invention collects 11 blood test indicators closely related to the musculoskeletal system, namely height, weight, body mass index (BMI), protein content, total body water, inorganic salt content, body fat content, fat-free mass, skeletal muscle content, basal metabolic rate, and waist-to-hip ratio, in a targeted manner. Computer models are built respectively for male and female data around the health data of the musculoskeletal system to construct computer models for predicting the physiological age of the male and female human musculoskeletal systems, so as to evaluate the physiological age of individuals of different genders in the human musculoskeletal system. A method for predicting the physiological age of the human musculoskeletal system based on machine learning of the present invention, because only the data indicators related to the human musculoskeletal system, such as height, weight, body mass index (BMI), protein content, total body water, inorganic salt content, body fat content, fat-free mass, skeletal muscle content, basal metabolic rate, and waist-to-hip ratio, are used as the core data for modeling to construct the physiological age model of the human musculoskeletal system, can specifically evaluate the physiological age of the human musculoskeletal system; moreover, a method for predicting the physiological age of the human musculoskeletal system based on machine learning of the present invention builds models separately for men and women based on the data differences between genders, making the prediction results more accurate.

[0036] The above is only a preferred embodiment of the present invention and is 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 musculoskeletal system based on machine learning, characterized in that: It includes the following steps: S1. Collect physical examination data of the human musculoskeletal system: The physical examination data of the human musculoskeletal system includes the gender, calendar age of the examinee, and musculoskeletal blood test data; the musculoskeletal blood test data includes: height, weight, body mass index (BMI), protein content, total body water, inorganic salt content, body fat content, fat-free body weight, skeletal muscle content, basal metabolic rate, and waist-hip ratio; S2. Divide the health examination data into a male dataset and a female dataset according to gender; S3. Divide the male dataset and the female dataset into a training set and a test set according to a certain proportion respectively; S4. Perform normalization on the data of the training set: Perform normalization on the data in the training sets of the male dataset and the female dataset respectively; S5. Build a model and conduct model training: Build models for the data in the training sets of the male dataset and the female dataset respectively, and perform cross-validation hyperparameter optimization by means of grid search; S6. Perform prediction and correction of the physiological age of the human musculoskeletal 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 intercept are calculated, and the specific calculation method is: Perform linear fitting of the original age difference and the calendar age 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.

2. The physiological age prediction method of the human musculoskeletal system based on machine learning according to claim 1, wherein: 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 method for predicting the physiological age of the human musculoskeletal 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 the human musculoskeletal system based on machine learning according to claim 1, characterized in that: In step S4, when performing normalization 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 the human musculoskeletal system based on machine learning according to claim 4, characterized in that: In step S4, when performing normalization on the data of the training set, the normalization method is maximum-minimum normalization, and the formula is x′ = (x − min(x)) / (max(x) − min(x)), where: x is the original value, min(x) is the minimum value in the dataset, max(x) is the maximum value in the dataset, and x′ is the value after correction.

6. A method for predicting the physiological age of the human musculoskeletal 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 the human musculoskeletal 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 dataset and the female dataset respectively, and cross-validation hyperparameter optimization is performed by means of 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 model.

8. A method for predicting the physiological age of the human musculoskeletal system based on machine learning according to claim 7, characterized in that: After step S54, the following steps are further included: S55. Perform evaluation and verification on the training set and / or the test set.

9. A method for predicting the physiological age of the human musculoskeletal system based on machine learning according to claim 7, characterized in that: Specifically, step S53 for performing hyperparameter tuning is to use GridSearchCV for hyperparameter tuning.

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