Di Zhao, Graduate, University of Connecticut
Sai Jahnavi Gamalapati, Graduate, University of Connecticut
Cardiovascular disease is the number one cause of death globally, claiming an estimated 17.9 million lives in 2019, accounting for 32% of all deaths worldwide that year.
Heart failure is a common illness of cardiovascular disease, and this dataset contains 11 features that can be used to predict likely heart disease. The prediction results can help people with cardiovascular disease or high cardiovascular risk (due to the presence of one or more risk factors, such as hypertension, diabetes, hyperlipidemia, or established diseases) to predict early symptoms and detect disease risk in a timely manner.
The data set included 918 participants from different countries and 11 factors associated with heart failure, such as age, sex, blood pressure, blood glucose, etc. This study plans to use different analysis models in JMP software for statistical analysis of data sets, such as neural networks, logistic classifiers, Random Forest, etc. The optimal prediction model is selected by comparing model performance.
The model output will help people understand the importance of different factors leading to heart disease and the probability of developing heart disease under certain conditions, to help people pay more attention to the management of physical health in daily life and the prediction of disease risk.
