A Grey Wolf-Based Clustering Algorithm for Medical Diagnosis Problems

Evolutionary and swarm intelligence algorithms are used as optimization algorithms for solving the clustering problem. One of the most popular optimization algorithms is the Grey Wolf Optimizer (GWO). In this chapter, we use GWO on seven medical data sets to optimize the initial clustering centroids represented by the individuals of each population at each iteration. The aim is to minimize the distances between instances of the same cluster to predict certain diseases and medical problems. The results show that solving the clustering task using GWO outperforms the other well-regarded evolutionary and swarm intelligence clustering algorithms, by converging toward enhanced solutions having low dispersion from the average values, for all the selected data sets.