ETMClustering

This project aims to evaluate the developmental status of countries by applying k-means clustering to categorize countries based on the primary causes of death. By analyzing global mortality data, the project assesses how the dominant causes of death—such as infectious diseases, chronic illnesses, or accidents—correlate with a country's level of development. The clustering results support the Epidemiologic Transition Model (ETM), which theorizes that as countries develop, the primary causes of death shift from infectious diseases to chronic, non-communicable diseases. This model highlights a country's progression through stages of public health and medical advancements, reflecting its socioeconomic and infrastructural growth. By using unsupervised machine learning, this project provides empirical evidence for the validity of the ETM across different regions and developmental stages.