Prediction of cardiovascular disease based on machine learning

Cardiovascular disease is one of the deadliest diseases worldwide, causing millions of deaths every year. Major risk factors include hypertension, hyperlipidemia, smoking, unhealthy diet, and lack of physical activity. To achieve a simple and effective prediction of cardiovascular disease, a study comparing the performance of common machine learning algorithms was conducted. The dataset used in this research consists of a population of 70,000 individuals from Kaggle. During the data processing phase, abnormal values within the feature variables were removed, and a BMI feature variable was added to the dataset to visualize the relationships between the data more intuitively. Deep neural networks were used to predict cardiovascular disease and were compared with eight traditional machine learning algorithms with respect to accuracy, F1 score, PR and ROC. The results indicated that the deep neural network (DNN) is the optimal model for predicting cardiovascular disease.