Enhanced Data Mining Techniques for Accurate Early Prediction of Cardiovascular Diseases Using Clinical Data

Cardiovascular disease (CVD) is the number one cause of death worldwide (Gaziano, Reddy et al. ۲۰۰۶). Heart disease is common both in the population at large and in the working-age population. It is estimated that heart disease, including stroke and high blood pressure, is responsible for more costs than any other disease or injury (Price ۲۰۰۴). Early detection and accurate diagnosis are therefore essential to reduce mortality and improve patient outcomes. In this study, clinical data from a cohort of patients were analyzed to develop predictive models capable of identifying the presence of heart disease. The dataset contained multiple demographic, clinical, and diagnostic attributes, which underwent thorough preprocessing, including handling missing values, encoding categorical variables, and normalization. Data visualization techniques were employed to explore feature distributions and relationships, aiding in better understanding of the dataset. Machine learning techniques were applied and evaluated using standard performance metrics. The results indicate that computational models can effectively distinguish between patients with and without heart disease, demonstrating their potential as decision-support tools in clinical settings. This approach highlights the value of data-driven methods in enhancing diagnostic accuracy and underscores the importance of integrating such technologies into routine medical practice. Future research will focus on expanding the dataset and exploring advanced learning algorithms to further improve predictive performance.

Keywords:

Cardiovascular Disease , Heart Disease Prediction , Machine Learning , Clinical Data Analysis , Early Diagnosis