Title: Diabetes risk stratification method based on fuzzy logic and bio-inspired meta-heuristics

Authors: Andrea Deme; Viorica R. Chifu; Cristina B. Pop; Emil St. Chifu; Ioan Salomie

Addresses: Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania ' Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Abstract: This paper presents a system for diabetes risk stratification that combines fuzzy logic with two bio-inspired algorithms. The developed system takes as input a set of patients described by numerical and categorical features and generates fuzzy rules to classify them into groups according to their risk of having diabetes. To take into consideration the uncertainty from the input dataset, our system combines fuzzy logic techniques with bio-inspired algorithms and hierarchical classification. The system has been evaluated on Pima Indians data from UCI Machine Learning Repository.

Keywords: ant clustering; CLONALG; fuzzy logic; risk stratification; diabetes.

DOI: 10.1504/IJCISTUDIES.2019.102577

International Journal of Computational Intelligence Studies, 2019 Vol.8 No.3, pp.223 - 244

Received: 24 May 2018
Accepted: 04 Oct 2018

Published online: 26 Sep 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article