An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease

Assisted Living provides a long-term care option that combines supportive systems andservices for monitoring and assessing the health status with activities of daily living andhealth care. Daily monitoring of the health status in subjects characterized by chronic and/ordegenerative conditions is not possible in all those cases where the disease progressionhas to be evaluated only by a direct interaction between the patients and the healthcarestructures on a regular basis, over time and for life. In this respect, this work proposes anevolutionary-fuzzy decision support system (DSS) for assessing the health status of subjectsaffected by multiple sclerosis (MS) during the disease progression over time. Such a DSS hasbeen defined and implemented exploiting a novel approach devised to facilitate the designof fuzzy DSSs for medical problems. The approach is aimed at: (i) introducing a set of designcriteria to encode the medical knowledge elicited from clinical experts in terms of linguisticvariables, linguistic values and fuzzy rules with the final aim of granting the interpretabil-ity; (ii) defining a fuzzy inference technique to best fit the structure of medical knowledgeand the peculiarities of the medical inference; (iii) defining an evolutionary technique totune the formalized knowledge by optimizing the shapes of the membership functions foreach linguistic variable involved in the rules. An experimental session has been carried outfor evaluating, first of all, the approach on five medical databases commonly diffused inliterature and for comparing it with other systems. After that, the evolutionary-fuzzy DSSfor assessing MS patient's health status has been quantitatively evaluated on 120 patientsaffected by MS and compared with other approaches. The achieved results have shown thatour approach is very effective on the five databases, since it provides, on average, the secondhighest accuracy when compared to eight tools. Furthermore, as far as the classification ofmultiple sclerosis lesions is considered, the proposed system has turned out to outperformnine popular tools.