Chronic obstructive pulmonary disease (COPD) is a high mortality disease, second to stroke and ischemic heart disease. This non-curable disease progressively exacerbates, leading to high personal and societal economic impact, reduced quality of life and often death. General treatment plans for COPD risk mistreating the individuals’ condition. To be effective, the treatment should be individualized following the practices of precision medicine. The aim of this thesis was to develop a data driven algorithm and system with visualization to assess individual COPD risk. With MRI body composition profile measurements, it is possible to accurately assess propensity of a multitude of metabolic conditions, such as coronary heart disease and type 2 diabetes.  The algorithm and system has been developed using Wolfram Language and R within the Wolfram Mathematica framework. The algorithm calculates individualized virtual control groups metabolically similar to the patient’s body composition and spirometric profile. Using UK Biobank data, our tool was used to assess patient COPD propensity using an individual-specific virtual control group with AUROC 0.778 (female) and 0.758 (men). Additionally, the tool was used to identify new body composition profiles related to COPD and associated comorbid conditions.