Medical data processing method for predicting cardiovascular disease

Abstract

The invention provides a medical data processing method for predicting a cardiovascular disease. The method of the invention comprises three steps of 1, data preprocessing, filling deletion values ina data set, and performing standardizing processing on the attribute in the data set; 2, performing density weight learning, based on a fact that the sample points are divided into core sample points,noise sample points and boundary sample points by means of a DBSCAN algorithm, further quantifying density information of the core sample points, and endowing different weights to points in the areaswith different densities; and 3, performing characteristic engineering, adding the weight values of all sample points as one-dimensional new characteristics into the data set, and then performing characteristic selection and data discretization on the whole data set. According to the medical data processing method, through endowing the corresponding weights to the sample points with different distribution densities, the contribution degree of the core sample point in model establishing is emphasized, thereby helping establishment of a machine learning model decision boundary, and improving cardiovascular disease predicting precision of the model.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a density weight machine learning and processing method for cardiovascular disease prediction. Background technique [0002] Cardiovascular and cerebrovascular diseases are the number one cause of death in the world. In 2015, 17.7 million people died of cardiovascular diseases worldwide, accounting for 31% of the total global deaths. Therefore, early diagnosis and prevention of cardiovascular disease is very necessary, however, there is no effective prediction method in clinical medicine at present. In the era of rapid development of information technology, applying advanced computer science and technology to medical data is a hot research direction at present. If the machine learning model is applied to cardiovascular diseases, it can predict the risk of disease more stably and accurately, identify high-risk individuals in advance, and prevent cardiovascular diseases, which is o...

AI Technical Summary

Problems solved by technology

Although a prediction model for cardiovascular disease or stroke recurrence has been established at home and abroad, the predictors are mainly based on traditional risk factors and fail to include the parameters of cerebrovascular disease detection, which directly affects its predictive performance

Foreign forecasting models still have differences in culture, economy and race, and are not suitable for extrapolation and application at home and abroad

[0004] In addition, there are still more problems in using machine learning methods to accurately predict the occurrence of cardiovascular diseases, such as: (1) There are missing values ​​in the original medical data, which needs to be properly screened and completed

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