How can an insurance company use K means clustering to research patients with diabetes?

Using K means clustering enables an insurance company to segment patients into distinct groups based on shared characteristics. By researching the character traits of the cluster with the highest number of diabetics, the company can gain insights into the demographics, behaviors, and potentially the lifestyle factors prevalent among that group. This targeted analysis helps the company understand risk factors associated with diabetes, allowing for more effective management of coverage, pricing, and tailored health initiatives aimed at this specific population.

Focusing on the broader data sets, like analyzing all insurance claims or focusing on customer complaints, may not yield specific insights into the diabetic population. Surveying all patients regardless of their condition would dilute the findings as it encompasses a diverse group that doesn’t provide concentrated insights into the diabetic segment specifically. Instead, clustering highlights patterns and characteristics related directly to diabetes, facilitating more effective healthcare strategies and interventions for that group.