Exercise scheme reactivity prediction model, modeling method and electronic equipment

Abstract

The invention discloses an exercise scheme reactivity prediction model, a modeling method and electronic equipment. The modeling method of the exercise scheme reactivity prediction model comprises thefollowing steps: S101, collecting training data and carrying out preprocessing on the training data, wherein the step of collecting the training data and preprocessing the training data comprises thesubsteps: collecting indexes and exercise information data of a metabolic disease patient, obtaining an exercise scheme effect by evaluating the indexes of the metabolic disease patient, and screening out significant influence factors by analyzing the effect of the exercise scheme; S102, setting a training parameter of the XGBoost; S103, training the model by adopting a ten-fold cross validationmethod on the basis of an XGBoost algorithm by taking the significant influence factors and the exercise information data as input of the model and taking the exercise scheme effect as the output of the model; and S104, performing prediction by using the model trained in the step S103. The established prediction model can assist a doctor in forming more effective exercise schemes, and the exerciseintervention effect is improved.

Description

technical field [0001] The invention relates to the fields of biomedical engineering technology and sports health, in particular to a predictive model and a modeling method for the reactivity of a sports program. Background technique [0002] The role of exercise in the prevention and treatment of metabolic diseases cannot be ignored. Studies have shown that moderate exercise can improve blood sugar, blood pressure and blood lipids in metabolic diseases. However, the current exercise programs prescribed by doctors have the following problems: [0003] (1) The effect of the exercise program is uncontrollable. Before prescribing the exercise program, the doctor only knows that the exercise program can improve the patient's metabolic condition, but it is not clear to what extent the exercise program can be improved. [0004] (2) The current exercise program cannot be targeted. For patients with the same disease, the exercise program prescribed by the doctor may be the same, bu...

AI Technical Summary

Problems solved by technology

[0003] (1) The effect of the exercise program is uncontrollable. Before prescribing the exercise program, the doctor only knows that the exercise program can improve the patient's metabolic condition, but it is not clear to what extent the exercise program can improve

[0004] (2) The current exercise program cannot be targeted. For patients with the same disease, the exercise program prescribed by the doctor may be the same, but due to the differences in other conditions of the individual, the actual effect is different. Therefore, for patients with the same disease The same exercise program, some patients have a good improvement effect, and some patients have a poor improvement effect

[0005] (3) The exercise program is too extensive. The doctor prescribes the user’s exercise intensity, exercise time, exercise frequency, and exercise type according to the guidelines, and fails to quantify the patient’s more exercise information, such as the choice of exercise time and excessive exercise. Time, ineffective exercise time, exercise efficiency and the impact of evaluating these exercise information on the intervention effect

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