Cardiovascular synthetic tabular data

Membership Inference Attack Accuracy: The dataset scored 0.6780 in this metric. Membership inference attack accuracy measures the likelihood of correctly inferring whether a particular data point was part of the training dataset. An accuracy of 0.6780 suggests that the model maintains a strong level of privacy.

It is important to note that a score of 0.5 would indicate random guessing, hence the achieved score demonstrates significantly better privacy protection than random predictions. Statistical Tests: Comprehensive statistical tests were conducted to compare the synthetic data with real data. These tests ensure that the synthetic data has similar statistical properties and distributions to the original data.

Machine Learning Efficiency: The utility of the dataset was also validated using machine learning models to ensure that the synthetic data is effective for training and can produce reliable predictive models. The results showed that models trained on this dataset performed well, reinforcing the practical utility of the data. The high DCR value and the membership inference attack accuracy highlight the balance between data utility and privacy, making this dataset an invaluable resource for researchers and practitioners focusing on cardiovascular diseases and machine learning.