A Novel Approach For Diagnosis Of Dermatologic Diseases Based On Multi-Objective Clustering Using Particle Swarm Optimization

Abstract

The stratification of skin diseases plays a major role in enabling effective and customized medicine. An important task in the stratification of skin diseases is to discover subtypes of diseases for effective treatment. To achieve this goal, research into cluster algorithms for the stratification of skin diseases has attracted the attention of both scholars and the medical community in recent decades. However, cluster algorithms suffer from realistic limitations such as experimental sounds, high dimension and poor interpretation. In particular, cluster algorithms usually determine the quality of clusters with only one internal evaluation operation. Unfortunately, it is obvious that one internal evaluation operation is difficult to create and strong for all datasets. Therefore, this article proposes a novel of many descriptive frameworks called a multi objective cluster algorithm with a quick search and finding the density of peaks to deal with these limitations altogether. In the proposed framework, the variable component of the candidate is developed under many objectives to select properties and estimate the density of the cluster automatically. To guide the development of multi-objective, two cluster value indices are used, including the Calinski-Harabasz index and the Davies-Bouldin index. The clustering is performed with the help of a k-means cluster by finding the best clusters using particle optimization. PSO will solve the multi-objective to determine the best cluster. Here, the proposed method will test on the dermatology dataset from UCI repository using MATLAB R2020a, under windows10 environment. Then, the proposed method performance will be evaluating using the cluster evaluation indices.