Predictive Modeling for Cardiovascular Health: Unveiling Insights, Enhancing Interpretability, and Charting Future Trajectories

Heart disease is still a major global health concern, which makes the creation of reliable and treatment necessary. This research study analyzes a large dataset that includes cardiovascular risk variables by using cutting-edge technologies by considering clinical measurements, lifestyle factors, and demographic data. The study uses well-known Python tools for data preprocessing, exploratory data analysis (EDA), and predictive modeling, including NumPy, pandas, and scikit-learn. Statistical and visual techniques are used in exploratory data analysis to find patterns and relationships present in the dataset. The relationships between gender, forms of chest discomfort, and the existence is investigated by using chi-squared testing and visualization approaches. The significance of several characteristics are also analyzed in this study. To determine possible risk factors, the relationship between categorical variables is measured using the Cramer’s V statistic. Correlation matrices are also utilized to investigate the connections between numerical variables. An extensive analysis of feature importance is also included in the conclusion, highlighting the factors that have a major impact on the prognosis. Bar charts are used as visualization tools to reveal the significance of different parameters in the prediction models.