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

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