Implementation of k-means and Hierarchical clustering methods to the Heart Attack Dataset

The identification and grouping of individuals based on these health indicators are crucial for understanding patterns that may correlate with heart attack risk. By partitioning the data into meaningful groups without relying on explicit outcome labels, the analysis highlights trends in health indicators and provides insights into natural groupings of health profiles. These findings can facilitate targeted interventions, improve risk stratification, and enhance understanding of patient heterogeneity, potentially guiding medical decision-making and identifying high-risk subpopulations for further study.

ZG2024