Ventricular hypertrophy prediction system and device and storage medium

Abstract

The invention discloses a ventricular hypertrophy prediction system and device and a storage medium. The system comprises an acquisition module used for acquiring electrocardiogram data of a to-be-predicted person in multiple lead modes; a grouping module, which is used for grouping the lead modes and dividing the electrocardiogram data according to a grouping result of the lead modes to obtain a plurality of electrocardiogram data groups; a prediction module, which is used for inputting the electrocardiogram data groups into different prediction models to obtain first prediction results corresponding to the electrocardiogram data groups, wherein the first prediction result is used for representing whether the person to be predicted has ventricular hypertrophy; and a processing module, which is used for obtaining a ventricular hypertrophy prediction result of the person to be predicted according to a weighted result of each first prediction result. The method can effectively overcome the problem of low prediction result precision caused by a single data source and a single prediction model. The method can be widely applied to the technical field of artificial intelligence.

Description

Technical field[0001]The present application relates to artificial intelligence technology, especially a prediction system, device and storage medium of ventricular hypertrophy.Background technique[0002]Ventricular hypertrophy is a syndicology disease, mainly caused by heavy duty (diastolic or systolic period), including ventricular hypertrophy and expansion, wherein the pressure load is mainly based on ventricular hypertrophy, capacity load The overweight is mainly expanded in ventricular: After the load is long, it is often hypertrophy and expansion. The ventricular hypertrophy is the common consequences of equipment-sex heart disease, which can be expressed on an electrocardiogram when reaching a certain degree.[0003]When performing ventricular hypertropactics, the electrocardiogram data in some patients and the electrocardiogram in normal state may have a distinctive difference. Artificially determine whether the patient has a ventricular fertilizer in accordance with the electr...

AI Technical Summary

Problems solved by technology

[0003] When diagnosing ventricular hypertrophy, there may be imperceptible differences between the ECG data of some patients and the ECG data under normal conditions. Artificially judging whether a patient has ventricular hypertrophy based on the ECG data requires a lot of clinical experience, and the obtained prediction results may not be as expected

In related technologies, there are cases of using machine learning models to predict whether patients have diseases, but the prediction results obtained by this model are not reliable, and misdiagnosis is easy to occur

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