In Europe senior citizens are a fast growing part of population, increasing proportion of disabled persons and that of persons with reduced quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on it, the World Health Organization (WHO) developed the clinical test WHO Disability Assessment Schedule, (WHO-DASII) that includes physical, mental, and social well-being, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge about performance of the WHO-DASII using a sample of patients from an italian hospital.

This Knowledge Discovery problem has been faced by using clustering based on rules, an hybrid AI and Statistics technique introduced by Gibert (1994), which combines some Inductive Learning (from AI) with clustering (from Statistics) to extract knowledge from certain complex domains in form of tipical profiles. In this paper, the results of applying this technique to the WHO-DASII results is presented together with a comparison of other more classical analysis approaches.