Sudden heart disease prediction method based on Transform-MHP model

Abstract

The invention discloses a sudden heart disease prediction method based on a Transform-MHP model. The method comprises four parts of data preprocessing, feature analysis, model construction and training and performance evaluation. Firstly, data preprocessing is carried out according to an obtained heart disease data sample, then a PCA principal component analysis method is used for carrying out dimension reduction analysis on a data set, and finally a Spearman correlation analysis algorithm is used for screening out fourteen feature attributes for model training. Herein, the main action range of the Transform algorithm is natural language processing and is remarkable; wherein the traditional Transform framework is improved and innovated, and a new Transform-MHP algorithm model is provided in combination with a high-expansibility parallel processing algorithm to be used in the AI medical field to predict the probability of the sudden heart disease so as to assist in improving the medical working efficiency and accuracy. Finally, the performance of the model is evaluated through experiments, and the result shows that compared with a traditional algorithm, the Transformer-MHP heart disease prediction algorithm has better accuracy and interpretability.

Description

technical field [0001] The invention belongs to the field of AI medical treatment, and in particular relates to a method for predicting sudden heart disease based on the Transformer-MHP model. Background technique [0002] Changes in population size and structure as well as uncontrollable environmental factors have led to increasing pressure on the medical industry year by year. However, with the breakthrough and promotion of artificial intelligence technology, its application scenarios are becoming more and more abundant and universal. With the help of high-performance and high-efficiency data processing advantages of computers, combined with big data analysis and deep learning, artificial intelligence has changed the medical status to a large extent, significantly reduced costs and improved efficiency. [0003] At present, machine learning algorithms such as MLP, decision tree, SVM, and K-Means have been used to build prediction models in the field of heart attack predict...

AI Technical Summary

Problems solved by technology

[0003] At present, machine learning algorithms such as MLP, decision tree, SVM, and K-Means have been used to build prediction models in the field of heart attack prediction, but the training results show that these algorithms have certain defects, and there is still room for improvement in model accuracy and efficiency. space

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