阅读说明：本技术 心房颤动人工智能实验选择指标的方法及预测决策树在房颤预测中的应用 (Method for selecting indexes through atrial fibrillation artificial intelligence experiment and application of prediction decision tree in atrial fibrillation prediction ) 是由 张树龙 张敏 杨慧英 冯雪颖 于 2018-09-13 设计创作，主要内容包括：心房颤动人工智能实验选择指标的方法及预测决策树在房颤预测中的应用,属于数据处理领域,为了解决选择更为准确反映心房颤动的指标的问题,包括S1.构建决策树；S2.调整参数以优化决策树；S3.对各种参数可能的取值均进行实验,最后选取最优实验结果,该结果作为决策树预测的主要指标,本发明通过人工智能及大数据处理,对房颤预测指标作出了更为合理的选择,该指标是经过大数据处理以得到的能够更为准确反映房颤的指标,使用这些指标评估房颤能降低对房颤对漏检。(A method for selecting indexes through an atrial fibrillation artificial intelligence experiment and application of a prediction decision tree in atrial fibrillation prediction belong to the field of data processing, and aim to solve the problem of selecting indexes which more accurately reflect atrial fibrillation, the method comprises the following steps of S1, constructing a decision tree; s2, adjusting parameters to optimize a decision tree; and S3, performing experiments on possible values of various parameters, and finally selecting an optimal experiment result as a main index of decision tree prediction.)

1. A method for selecting indexes in an atrial fibrillation artificial intelligence experiment is characterized by comprising the following steps:

s1, constructing a decision tree;

s2, adjusting parameters to optimize a decision tree;

and S3, performing experiments on possible values of various parameters, and finally selecting an optimal experiment result which is used as a main index for decision tree prediction.

2. The method of selecting an indicator for atrial fibrillation artificial intelligence experiments according to claim 1, wherein: the main indexes are three attributes of A peak, ef and last in the attributes of the cardiac ultrasound.

3. The method of selecting an indicator for atrial fibrillation artificial intelligence experiments according to claim 1, wherein: the main indexes are XGN (cardiac function grade), A peak (cardiac ultrasonic index), FS (rheumatic heart valve disease), FJB (interstitial lung disease), LVPWD (cardiac ultrasonic index), EF (cardiac ultrasonic index), FDMB1 (pulmonary valve blood flow velocity), FDMB (pulmonary valve), LAD (cardiac ultrasonic index), GXB (coronary heart disease), TNB (diabetes), MCHC (hemoglobin concentration) and E peak (cardiac ultrasonic index).

4. The method of selecting an indicator for atrial fibrillation artificial intelligence experiments according to claim 1, wherein: the method for constructing the decision tree comprises the following steps:

step 1: if the data set S belongs to the same category, a leaf node is created, a corresponding category label is marked, and the tree building is stopped; otherwise, performing the step 2;

step 2: calculating information Gain rates Gain-rate (A) of all attributes in the data set S;

and step 3: selecting an attribute A of the maximum information gain rate;

and 4, step 4: establishing the attribute A as a root node of a decision tree T, wherein the T is a decision tree to be established;

and 5: dividing the data set into a plurality of subsets according to different values of the attribute A, circularly executing the steps 1-4 on the subset Sv, and constructing a subtree Tv, wherein the Sv is a sample subset with the value of the attribute A being v;

step 6: adding the subtree Tv to the corresponding branch of the decision tree T;

and 7: and (5) finishing the circulation to obtain a decision tree T.

5. An application of a prediction decision tree in atrial fibrillation prediction.

Technical Field

The invention belongs to the field of data processing, and relates to a method for constructing an atrial fibrillation prediction decision tree and a method for selecting indexes in an atrial fibrillation artificial intelligence experiment.

Background

Atrial fibrillation is a supraventricular tachyarrhythmia characterized by rapid, chaotic electrical atrial activity. Atrial fibrillation is mainly shown on an electrocardiogram by disappearance of P waves and replacement with irregular atrial fibrillation waves; RR intervals are absolutely irregular (when atrioventricular conduction is present). This is also the main basis for judging atrial fibrillation in medical field and the like at present. Atrial fibrillation is medically classified mainly into paroxysmal atrial fibrillation (paroxysmal AF), persistent atrial fibrillation (persistent AF), long-range persistent atrial fibrillation (long-standing persistent AF), and permanent atrial fibrillation (persistent AF) according to the duration of an episode of atrial fibrillation. The specific classification is shown in Table 1.

TABLE 1.1 detailed classification of atrial fibrillation in medicine

Atrial fibrillation is a very common arrhythmia in clinic, the incidence rate of the atrial fibrillation in China is 0.5% -1%, and the incidence probability is higher with the increase of age. The risk of atrial fibrillation of the hypertensive patients is 1.7 times higher than that of the normotensive patients, and at present, 33 percent of patients with atrial fibrillation are caused by hypertension. In response to the high incidence of atrial fibrillation in hypertensive patients, it is even thought that atrial fibrillation is another manifestation of damage to the target organs of hypertension. But at present, no better index exists clinically for predicting the occurrence of AF of hypertension patients. In addition, some patients with atrial fibrillation do not have obvious clinical symptoms, so that the patients are unconsciously exposed to the risks of various critical diseases, and when clinical symptoms appear or the diseases are sudden, cardiovascular organic lesions are often caused, so that the physical health of the patients is greatly influenced and even the life of the patients is threatened. Therefore, it is very important to study the probability of atrial fibrillation in the population of hypertensive patients.

At present, a plurality of methods for predicting atrial fibrillation exist, and the method starts from the aspect of treatment of atrial fibrillation in the medical field. Although CHA exists internationally₂DS₂The VASc score (hypertension, age, diabetes, stroke, vasculopathy, gender, congestive heart failure) and the hach score (hypertension, age, onset of cerebral ischemia, chronic obstructive pulmonary disease, heart failure) are used to predict atrial fibrillation, but both of these scores have various limitations that make the prediction method non-normative and the prediction result inaccurate. In the field of computers, it is common to use the electrocardiogram of the patient, to determine the P-wave and to analyze the RRThe variation rule of interval distribution along with time and other factors are used for judging whether the patient has atrial fibrillation, and the used algorithm has the aspects of statistics and machine learning. Some characteristic indexes of a human body are detected through a smart watch for prediction, the face is scanned through a smart phone for prediction through the face color of the human body, and even for asymptomatic patients, the Holter heart rate of the patients is directly tested through a medical instrument for prediction. These are still lacking in standardization and have no particular standard.

Disclosure of Invention

In order to solve the problem of selecting an index which more accurately reflects atrial fibrillation, the invention provides the following scheme:

a method for selecting indexes in an atrial fibrillation artificial intelligence experiment comprises the following steps:

s1, constructing a decision tree;

s2, adjusting parameters to optimize a decision tree;

and S3, performing experiments on possible values of various parameters, and finally selecting an optimal experiment result which is used as a main index for decision tree prediction.

Further, the main indexes are three attributes of A peak, ef and last in the attributes of the cardiac ultrasound.

Further, the main indexes are XGN (cardiac function grade), peak a (cardiac ultrasound index), FS (rheumatic valvular heart disease), FJB (interstitial lung disease), LVPWD (cardiac ultrasound index), EF (cardiac ultrasound index), FDMB1 (pulmonary valve blood flow velocity), FDMB (pulmonary valve), LAD (cardiac ultrasound index), GXB (coronary heart disease), TNB (diabetes), MCHC (hemoglobin concentration), peak E (cardiac ultrasound index).

Further, the method for constructing the decision tree is as follows:

step 1: if the data set S belongs to the same category, a leaf node is created, a corresponding category label is marked, and the tree building is stopped; otherwise, performing the step 2;

step 2: calculating information Gain rates Gain-rate (A) of all attributes in the data set S;

and step 3: selecting an attribute A of the maximum information gain rate;

and 4, step 4: establishing the attribute A as a root node of a decision tree T, wherein the T is a decision tree to be established;

and 5: dividing the data set into a plurality of subsets according to different values of the attribute A, circularly executing the steps 1-4 on the subset Sv, and constructing a subtree Tv, wherein the Sv is a sample subset with the value of the attribute A being v;

step 6: adding the subtree Tv to the corresponding branch of the decision tree T;

and 7: and (5) finishing the circulation to obtain a decision tree T.

The invention also relates to application of the prediction decision tree in atrial fibrillation prediction.

Has the advantages that: the invention makes more reasonable selection on the atrial fibrillation prediction index through artificial intelligence and big data processing, the index is obtained through big data processing and can more accurately reflect the index of atrial fibrillation, the indexes are used for evaluating the atrial fibrillation and reducing the missed detection of the atrial fibrillation pair.

Drawings

FIG. 1 is a schematic diagram of a decision tree structure;

FIG. 2 is a schematic illustration of a medical data manuscript;

FIG. 3 is a schematic diagram of a derived Excel table;

FIG. 4 is a schematic representation of cardiac ultrasound properties;

FIG. 5 is a schematic view of a 4weka operating interface;

FIG. 6 is a schematic diagram of decision trees each using default values;

FIG. 7 is a schematic diagram of decision tree accuracy;

FIG. 8 is a schematic diagram of a decision tree of 154 factors;

FIG. 9 is a schematic diagram of decision tree accuracy.

Detailed Description