Design of Interval Type-3 Fuzzy Inference Systems for Medical Classification using a Salp Swarm Algorithm

Abstract

Detecting diseases in early stages allows
patients to have a higher probability of success in
their treatments, in addition to reducing treatment costs,
which usually increase in advanced stages of various
diseases. This is why intelligent techniques have
recently become increasingly valuable for physicians,
as they are capable of detecting patterns that a
physician might not see or miss. This work proposes
the optimization of Interval Type-3 fuzzy systems
for disease classification. The structure of these
classifiers is optimized using the Salp Swarm Algorithm,
which searches the parameters of the membership
functions of each input and their corresponding fuzzy
rules. These optimized classifiers are designed using
three databases: Immunotherapy, Cryotherapy, and
Haberman’s Survival, and where the average accuracy
achieved is 84.72, 89.17, and 76.64, respectively. The
results accomplished are compared with Type-1 and
Interval Type-2 fuzzy systems designed by the same
optimization algorithm, and with fuzzy systems designed
using a different optimization technique.