Early prediction method for gestational diabetes mellitus and coping scheme

Abstract

The invention discloses an early prediction method for gestational diabetes mellitus and a coping scheme, and relates to the technical field of early prediction of diabetes mellitus. The prediction method comprises the following steps of S1, data collection, S2, classification modeling, S3, feature selection, S4, model establishment and S5, prediction. The scheme in the invention comprises the following schemes: W1, regular examination, W2, diet control, W3, exercise intervention, W4, drug treatment and W5, personalized scheme. The early prediction method for gestational diabetes mellitus and the coping scheme provided by the invention are an efficient and accurate treatment mode, can help pregnant women to find and cope with gestational diabetes mellitus in time, and protect the health of mothers and infants; a complete and effective gestational diabetes mellitus management system can be formed through the steps of data acquisition, classification modeling, feature selection, model establishment, prediction, regular inspection, diet control, exercise intervention, drug treatment, personalized schemes and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of early prediction of diabetes, and in particular to an early prediction method and a coping plan for gestational diabetes. Background Art

[0002] Gestational diabetes refers to a temporary hyperglycemia that occurs during pregnancy, usually in the late stages of pregnancy. This condition is related to a decrease in the insulin secretion level and insulin sensitivity of pregnant women, which increases the blood sugar level of pregnant women. Gestational diabetes has adverse effects on both the mother and the fetus, including complications such as high blood pressure, preeclampsia, and dystocia in pregnant women; the fetus may have problems such as macrosomia, hypoglycemia, respiratory distress syndrome, and congenital malformations. The main methods for treating gestational diabetes include controlling diet, increasing exercise, and injecting insulin. At the same time, pregnant women should monitor their blood sugar regularly during pregnancy to ensure that their blood sugar levels are within the normal range to protect the health of mother and child.

[0003] In today's society, gestational diabetes has become a common gynecological disease, which has a great impact on pregnant women and fetuses. Therefore, it is very important to predict and deal with gestational diabetes in a timely manner. The present invention will introduce a novel early prediction method and a coping plan for gestational diabetes that are different from the prior art. Summary of the invention

[0004] The purpose of the present invention is to solve the problems raised in the above background technology, and provide an early prediction method and a coping plan for gestational diabetes.

[0005] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions: An early prediction method for gestational diabetes, the method comprising the following steps: S1. Data collection: Collect information about pregnant women’s living habits and family medical history, and record them in combination with the fetus’s condition; S2. Classification modeling: By analyzing and comparing a large amount of data, a prediction model is established and pregnant women are classified according to the model; S3. Feature selection: From the collected data, statistical methods and correlation analysis were used to screen out features related to gestational diabetes and determine which features played an important role in the occurrence of gestational diabetes; S4. Model building: Based on the selected features, an early prediction model is built and the model is trained to improve the prediction accuracy; S5. Prediction: Prediction is performed based on the established model, and pregnant women are divided into high-risk and low-risk categories.

[0006] Preferably, in step S1, the collected data include the pregnant woman's age, BMI index, eating habits, weight gain, blood sugar level, and whether there is a history of diabetes in the family.

[0007] Preferably, in step S2, a machine learning algorithm is used to build a model for the collected data, and the collected data is classified and processed to obtain high-risk groups and low-risk groups respectively.

[0008] Preferably, in step S3, the characteristics include BMI index, eating habits, and blood sugar status.

[0009] Preferably, in step S4, multiple machine learning algorithms such as logistic regression, decision tree and neural network, as well as technical means such as cross-validation and parameter adjustment are used to further improve model performance.

[0010] Preferably, in step S5, by inputting the age, BMI index, and dietary habit information of the pregnant woman, the model outputs a prediction result indicating whether the pregnant woman is in a high-risk state.

[0011] An early response plan for gestational diabetes includes the following: W1. Regular check-up: Regular check-up of high-risk pregnant women; W2. Diet control: Control the diet of high-risk pregnant women and scientifically match nutrition; W3. Exercise intervention: Carry out appropriate exercise intervention according to the physical condition of pregnant women; W4. Drug treatment: For pregnant women whose blood sugar cannot be controlled by diet and exercise, drug treatment is used to control it; W5. Personalized plan: Develop a personalized treatment plan based on the individual differences of pregnant women.

[0012] Preferably, in scheme W1, the examination items include blood sugar test, urine sugar test, fetal ultrasound examination, blood pressure test and urine routine test, and the examination frequency is once a week or every two weeks.

[0013] Preferably, in scheme W3, the exercise intervention includes walking, yoga, swimming, exercise ball and bicycle.

[0014] Preferably, in scheme W4, pregnant women should be treated with appropriate medications under the guidance of a doctor, and their blood sugar levels and physical reactions should be closely monitored. Beneficial Effects

[0015] The early prediction method and response plan for gestational diabetes mellitus proposed in the present invention is an efficient and accurate processing method, which can help pregnant women detect and respond to gestational diabetes mellitus in a timely manner and protect the health of mother and baby; through the steps of data collection, classification modeling, feature selection, model building, prediction, regular examination, diet control, exercise intervention, drug treatment and personalized plan, a complete and effective gestational diabetes management system can be formed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the process of the early prediction method of the present invention; Figure 2 It is a schematic diagram of an early response solution of the present invention. DETAILED DESCRIPTION

[0018] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0019] The present application provides an early prediction method and a coping plan for gestational diabetes, which is mainly used to solve the problem that gestational diabetes has become a common gynecological disease, which has a great impact on pregnant women and fetuses, and provides the following technical solutions. Figure 1-Figure 2 Make a detailed description: Embodiment 1:

[0020] An early prediction method for gestational diabetes, the method comprising the following steps: S1. Data collection: Collect information about pregnant women’s living habits and family medical history, and record them in combination with the fetus’s condition; S2. Classification modeling: By analyzing and comparing a large amount of data, a prediction model is established and pregnant women are classified according to the model; S3. Feature selection: From the collected data, statistical methods and correlation analysis were used to screen out features related to gestational diabetes and determine which features played an important role in the occurrence of gestational diabetes; S4. Model building: Based on the selected features, an early prediction model is built and the model is trained to improve the prediction accuracy; S5. Prediction: Prediction is performed based on the established model, and pregnant women are divided into high-risk and low-risk categories.

[0021] In step S1, data collected include the pregnant woman's age, BMI index, eating habits, weight gain, blood sugar level, and whether there is a family history of diabetes. A glucose tolerance test is also required. The glucose tolerance test is a standard test for diagnosing gestational diabetes. Pregnant women drink a certain amount of sugar-containing aqueous solution on an empty stomach, and then measure it within a certain period of time to measure the pregnant woman's glucose tolerance. Pregnant women need to monitor themselves, and the monitoring items include blood sugar monitoring, nutrition records, weight records and medical history records. Blood sugar monitoring is used to understand whether their blood sugar levels are within the target range. Doctors instruct pregnant women to use blood glucose meters for monitoring; pregnant women need to record the types and amounts of food they consume every day; pregnant women need to measure their weight regularly and record it, which will help doctors understand the weight changes of pregnant women and facilitate the formulation of subsequent plans; pregnant women need to inform doctors of their medical history, including whether they have a family history of diabetes and whether they suffer from other chronic diseases. This information is very important for formulating personalized treatment plans.

[0022] In step S2, a machine learning algorithm is used to build a model for the collected data, and the collected data is classified and processed to obtain high-risk and low-risk groups. Through classification modeling, pregnant women can be divided into different risk groups and their possibility of developing gestational diabetes can be predicted.

[0023] In step S3, the features include BMI index, eating habits, blood sugar status, maternal age, family history of diabetes, maternal blood pressure data, maternal exercise habits and maternal insulin sensitivity. In the classification modeling of gestational diabetes, feature selection is to select the most predictive features from the available features for building a classification model. Selecting appropriate features can improve the accuracy and generalization ability of the classification model. The feature selection methods include: 1. Filtering feature selection: This method evaluates and sorts the features before modeling, and then selects the most representative features. The method used is the Pearson correlation coefficient; 2. Wrap-up feature selection: This method directly uses a classifier to evaluate the quality of the features, and finally determines the best feature subset by repeatedly training the model and excluding features. The method used is recursive feature elimination; 3. Embedded feature selection: This method embeds the feature selection process into the model training process.

[0024] In step S4, multiple machine learning algorithms such as logistic regression, decision tree and neural network, as well as technical means such as cross-validation and parameter adjustment are used to further improve the model performance; logistic regression is a commonly used classification algorithm, which can predict whether a pregnant woman has a risk of gestational diabetes based on known feature data. Logistic regression is based on a linear regression model, and by appropriately converting and processing the feature data, continuous prediction results are mapped to a binary classification problem; a decision tree is a classification method based on a tree structure, which can branch and judge according to feature values. In the classification modeling of gestational diabetes, the decision tree can make judgments and classifications based on various characteristics of pregnant women, such as age, body mass index, blood pressure, etc.; these classification modeling methods can be trained and verified based on existing data, and predict and classify new pregnant women's data. Classification modeling can help doctors better identify high-risk pregnant women, take intervention measures as early as possible, provide personalized treatment plans, reduce the incidence of gestational diabetes, and protect the health of mothers and babies.

[0025] In step S5, by inputting the age, BMI index, and dietary habits of the pregnant woman, the model outputs a prediction result indicating whether the pregnant woman is in a high-risk state. Before the prediction, the new pregnant woman data needs to be preprocessed, including data cleaning, missing value processing, feature standardization, etc., to ensure the quality and consistency of the data. According to the selected features, the corresponding feature values ​​are extracted from the new pregnant woman data, and the preprocessed feature data can be input into the trained classification model. The model will calculate according to the combination and weight of the feature values ​​and give a prediction result, that is, to determine whether the pregnant woman has a risk of gestational diabetes; The prediction results can be classified into two categories, that is, whether the pregnant woman belongs to the high-risk group or the low-risk group; it can also be a probability value, indicating the possibility of the pregnant woman developing gestational diabetes. It should be noted that the prediction results are only estimates based on existing data and training models. The specific diagnosis and treatment still need to be judged and decided by the doctor based on the clinical situation. The prediction results can be used as an auxiliary reference to help doctors better understand the health status of pregnant women, take appropriate intervention measures, and provide personalized treatment plans. Embodiment 2:

[0026] An early response plan for gestational diabetes includes the following: W1. Regular check-up: Regular check-up of high-risk pregnant women; W2. Dietary control: For high-risk pregnant women, dietary control should be carried out and nutrition should be scientifically matched. Pregnant women should choose low-sugar fruits, such as apples, oranges, strawberries and blueberries, and avoid high-sugar fruits, such as bananas, grapes and pineapples. At the same time, they should consume enough vegetables, fruits, whole grains and beans and other high-fiber foods, which can help control blood sugar levels and slow down the rise of blood sugar. They should control carbohydrate intake, avoid consuming large amounts of high-sugar foods and refined carbohydrates, and choose low-GI (glycemic index) foods such as whole grains, brown rice, whole wheat bread and whole wheat pasta. They should spread out their diet, eat multiple times a day, and eat moderate portions at each meal. They should avoid overeating. They should keep a diet record and record blood sugar levels before and after meals, which can help monitor the impact of food on blood sugar and adjust their diet plans. These food choices and dietary control strategies can help control blood sugar levels and prevent or alleviate the symptoms of gestational diabetes. W3. Exercise intervention: Carry out appropriate exercise intervention according to the physical condition of pregnant women; W4. Drug treatment: For pregnant women whose blood sugar cannot be controlled by diet and exercise, drug treatment is used to control it; W5. Personalized plan: Develop personalized treatment plans based on individual differences of pregnant women; Based on the predicted results of pregnant women, doctors develop specific diet plans, including reasonable diet combinations, controlling carbohydrate and sugar intake, etc., which help control blood sugar levels and prevent the development of gestational diabetes; Doctors provide pregnant women with appropriate exercise guidance and recommend moderate aerobic exercise, such as walking, swimming, etc. Exercise can help control blood sugar levels, increase the body's metabolic rate, and improve insulin sensitivity; For high-risk pregnant women, doctors recommend blood sugar monitoring to track changes in blood sugar levels and adjust diet and exercise plans in a timely manner. Monitoring blood sugar can help doctors understand the blood sugar control of pregnant women and determine whether drug treatment is needed; For pregnant women with unsatisfactory blood sugar control, doctors use oral medications or insulin treatment, and choose appropriate medications and dosages according to the specific situation of the pregnant woman; Pregnant women need regular checkups, including blood sugar testing, weight monitoring, etc., as well as follow-up with doctors. Regular checkups and follow-ups can timely evaluate changes in the pregnant woman's condition, adjust treatment plans, and provide necessary support and education.

[0027] In Plan W1, the examination items include blood sugar test, urine sugar test, fetal ultrasound, blood pressure test and urine routine test, and the examination frequency is once a week or every two weeks; Blood sugar monitoring: By measuring the blood sugar level in the blood, the blood sugar control of pregnant women is evaluated. Pregnant women use blood glucose meters for self-monitoring, or have blood sugar tests in medical institutions, record the data and provide them to doctors; Urine sugar test: By detecting the sugar in the urine, the blood sugar control of pregnant women is evaluated. Urine sugar test can help detect signs of elevated blood sugar; Fetal ultrasound: By observing the growth and development of the fetus through ultrasound technology, fetal ultrasound can detect whether the fetus has abnormal conditions, such as macrosomia, intrauterine growth restriction, etc., and evaluate the health status of the fetus; Blood pressure monitoring: By measuring the blood pressure level of pregnant women, it is evaluated whether the pregnant women have high blood pressure. High blood pressure is one of the common complications of gestational diabetes; Urine routine test: By detecting indicators such as protein, red blood cells, and white blood cells in the urine, the renal function and urinary system of pregnant women are evaluated. Urine routine test can help detect abnormal conditions such as proteinuria and urinary tract infection in pregnant women; These examinations can provide important information about the health status of the pregnant woman and fetus, help monitor the development and progression of gestational diabetes, and take appropriate treatment measures. The specific examination content and frequency should be determined based on the individual situation of the pregnant woman and the doctor's advice.

[0028] In scheme W3, exercise interventions include walking, yoga, swimming, fitness balls, and bicycles; walking is a simple and effective form of exercise that can help pregnant women control their blood sugar levels. Pregnant women should take a light walk for about 30 minutes a day and avoid strenuous exercise and standing for long periods of time; yoga is a gentle form of exercise that can relieve pregnant women's anxiety and stress and help control their blood sugar levels. Pregnant women should choose yoga poses that suit them and avoid overstretching and bumping; swimming is a non-load aerobic exercise that can help pregnant women exercise their whole body muscles, promote blood circulation, and reduce the burden on the back and legs; fitness balls are a relatively safe fitness tool that can help pregnant women perform balance training and core stability training while protecting joints and ligaments; cycling is an aerobic exercise that can help pregnant women exercise their cardiopulmonary function and muscle strength; When choosing a suitable exercise method, pregnant women should determine it based on their physical condition and pregnancy condition, consult a professional doctor or nutritionist, and choose the appropriate time and place to exercise. At the same time, pregnant women should avoid strenuous exercise, long periods of standing, and high temperature environments to ensure safety and health.

[0029] In plan W4, pregnant women need to use appropriate medications for treatment under the guidance of a doctor, and their blood sugar levels and physical reactions must be closely monitored. For some pregnant women with gestational diabetes who cannot control blood sugar through diet and exercise, insulin injection is a common treatment method. Insulin can help regulate blood sugar levels and keep them within the normal range. Pregnant women need to monitor their blood sugar levels themselves to understand whether their blood sugar levels are within the target range. Pregnant women are guided to use blood glucose meters for self-monitoring and adjust their diet and insulin dosage based on the monitoring results.

[0030] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for early prediction of gestational diabetes, characterized in that: The method comprises the following steps: S1. Data collection: Collect information about pregnant women’s living habits and family medical history, and record them in combination with the fetus’s condition; S2. Classification modeling: By analyzing and comparing a large amount of data, a prediction model is established and pregnant women are classified according to the model; S3. Feature selection: From the collected data, statistical methods and correlation analysis were used to screen out features related to gestational diabetes and determine which features played an important role in the occurrence of gestational diabetes; S4. Model building: Based on the selected features, an early prediction model is built and the model is trained to improve the prediction accuracy; S5. Prediction: Prediction is performed based on the established model, and pregnant women are divided into high-risk and low-risk categories.

2. The method for early prediction of gestational diabetes mellitus according to claim 1, characterized in that: In step S1, data collected include the pregnant woman's age, BMI index, eating habits, weight gain, blood sugar status, and whether there is a history of diabetes in the family.

3. The method for early prediction of gestational diabetes according to claim 2, characterized in that: In step S2, a machine learning algorithm is used to build a model for the collected data, and the collected data is classified and processed to obtain high-risk and low-risk groups respectively.

4. The method for early prediction of gestational diabetes according to claim 3, characterized in that: In step S3, the features include BMI index, eating habits, and blood sugar status.

5. The method for early prediction of gestational diabetes according to claim 4, characterized in that: In step S4, multiple machine learning algorithms such as logistic regression, decision tree and neural network, as well as technical means such as cross-validation and parameter adjustment are used to further improve the model performance.

6. The method for early prediction of gestational diabetes according to claim 5, characterized in that: In step S5, by inputting the pregnant woman's age, BMI index, and dietary habits information, the model outputs a prediction result indicating whether the pregnant woman is in a high-risk state.

7. An early response plan for gestational diabetes, characterized in that: Includes the following solutions: W1. Regular check-up: Regular check-up of high-risk pregnant women; W2. Diet control: Control the diet of high-risk pregnant women and scientifically match nutrition; W3. Exercise intervention: Carry out appropriate exercise intervention according to the physical condition of pregnant women; W4. Drug treatment: For pregnant women whose blood sugar cannot be controlled by diet and exercise, drug treatment is used to control it; W5. Personalized plan: Develop a personalized treatment plan based on the individual differences of pregnant women.

8. An early response plan for gestational diabetes according to claim 7, characterized in that: In plan W1, the examination items include blood sugar test, urine sugar test, fetal ultrasound examination, blood pressure test and urine routine test, and the examination frequency is once a week or every two weeks.

9. An early response plan for gestational diabetes according to claim 7, characterized in that: In protocol W3, the exercise intervention included walking, yoga, swimming, exercise ball, and cycling.

10. An early response plan for gestational diabetes according to claim 7, characterized in that: In plan W4, pregnant women need to use appropriate medications under the guidance of a doctor, and their blood sugar levels and physical reactions must be closely monitored.

Images