Abstract
Cardiovascular vascular disease (CVD) is a leading cause of death in the world. By 2025, it is estimated that 23.6 million people will be attacked by CVD. Thus, the health care industry is established in order to collect a large number of CVD information of cardiovascular disease and to support doctors in finding and recognizing its potential risk factors of CVD through mining and analyzing the information. This structured and unstructured case information may be used to find out potential patterns of diseases and symptoms using deep learning algorithms. The risk factors associated with cardiovascular disease are known from epidemiology, and this is the first prospective investigation on the condition in the community free mobility population. Physicians can anticipate cardiovascular disease and take early action if it can be predicted. Clinical data analysis is therefore considered to be the prediction of cardiovascular disease. The main contents and contributions are as follows: We start off with trying to predict using the classic data mining and machine learning techniques, and the results are not at all ideal. Generally speaking, after analysis, it is mainly caused by imbalance of data sets. To solve the problem of CVD data imbalance, a SMOTE oversampling method is proposed aiming at the imbalance of cardiovascular data collected in the Framingham community. In order to ensure accurate data collection during operation, the relationship between LSTM(P) and unit state is then tried, and the prediction technique of cardiovascular disease utilising LSTM(P) is realised. Lastly, tests are conducted to confirm the 4434 individuals' initial medical information in the data set. The algorithm has an MCC score of 0.96 and an accuracy of about 94%.