Clustering Medical Data to Predict the Likelihood of Diseases We identified 934 patients and used clustering analysis to group patients based on their medical data such as diagnoses, treatments, and symptoms. This allowed us to identify subgroups of patients who were likely to develop specific diseases. For example, clustering analysis may identify that patients being treated for fatigue and blurred vision have a strong likelihood of being diagnosed with diabetes. Clustering the patients in this way could help predict disease development and determine effective prevention and treatment strategies. Overall, clustering analysis can help optimize healthcare for both individuals and populations.