Prediction of Cardiovascular Diseases based on Machine Learning

Abstract

With the rapid development of artificial intelligence, it is very important to find the pattern of the data from the observed data and the functional dependency relationship between the data. By finding the existing functional dependencies, we can classify and predict them. At present, cardiovascular disease has become a major disease harmful to human health. As a disease with high mortality, the prediction problem of cardiovascular disease is becoming more and more urgent. However, some computer methods are mainly used for disease detection rather than prediction. If the computer method can be used to predict cardiovascular disease in advance and treat it as early as possible, then the consequences of the disease can be reduced to a certain extent. Diseases can be predicted by mechanical methods. Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. Therefore, support vector machine can be used to predict cardiovascular disease. On the other hand, we also use logical regression and random forest to predict cardiovascular disease. This paper mainly uses the method of machine learning to predict whether the population is sick or not. First of all, we preprocess the obtained data to improve the quality of the data, and then use svm and logical regression to predict, so as to provide reference for the prevention and treatment of cardiovascular diseases.

[Retracted] Application of Uncertainty Thought Environment in Judicial Adjudication Based on Cognitive Psychology

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ARIMA Model and Few-Shot Learning for Vehicle Speed Time Series Analysis and Prediction

“…Comparing the degree of difference between the curves in Figures 4 and 5 , it can be seen that the prediction model [ 20 ] established by using the first 40 data in the vehicle speed series obtained from the experiment has a better prediction effect on the last 20 vehicle speeds.…”

“…Literature [ 19 ] puts forward two strategies of setting left-turn phase and left-turn waiting area at intersections to improve the traffic quality at intersections. Literature [ 20 ] found that the intersection conflict simulated by software or model is very suitable for the actual intersection conflict research. Literature [ 21 ] holds that the occurrence of traffic accidents is mainly caused by the driver's mistakes, and the driver carries out a series of driving actions for a certain purpose, but the original driving intention cannot be realized due to the operation mistakes.…”

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

“…The research community has applied various classifiers for CVDs prediction such as Logistic Regression [ 40 , 73 , 74 ], Support Vector Machine [ 40 , 73 , 74 , 75 , 76 , 77 ], Naive Bayes [ 40 , 73 , 75 , 76 ], Neural Networks (NN) [ 75 ], k-Nearest Neighbours [ 75 , 76 ], Decision trees (DT) [ 75 ], AdaBoost [ 77 ], Random Forests [ 40 , 73 , 74 , 75 ], Language Model (LM) [ 75 ] and Gradient Boosting (GB) [ 73 , 77 , 78 ]. In Table 11 , we demonstrate a brief description of recent studies.…”