A Prediction Model of Cardiovascular Diseases of Elderly Based on Support Vector Machine Combined with Particle Swarm Optimization

Cardiovascular diseases (CVD) are common for elderly patients. With CVDs, patients often suffer greatly. The incubation period of CVD is generally long. Thus, before the onset of the disease, it is necessary to test the risk of the disease and detect it in time for corresponding treatment. This effectively reduces the impact of CVD on elderly patients. Considering the diversity and complexity of the clinical features of CVD, we used the support vector machine (SVM) and particle swarm optimization (PSO) for machine learning. Experimental verification showed that this method helped the elderly analyze the risk probability of CVD and reduce its harm.