The quality of data is a critical factor in healthcare analytics, particularly when dealing with sensitive medical information. In this study, we explore the analysis and cleaning of the Post-Myocardial Infarction (MI) Complications Dataset, with a focus on missing values and their impact on data integrity. We conduct a thorough analysis of missing values, identify the percentage of missing data for key features, and then employ two imputation methods: one is a hybrid of various traditional imputation techniques, tailored to the nature of each feature, and the other is a more sophisticated technique based on Copula theory, which captures interdependencies between variables to provide more accurate imputations. To compare the two methods, we use different metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). We then select the best-performing method to ensure minimal distortion of clinically relevant information. Especially in the context of medical data, an accurate handling of missing values is essential to maintain the integrity and reliability of the analysis. By providing a detailed approach to missing value handling, we aim to improve the reliability of healthcare data for use in future predictive models and clinical decision-making.