A hybrid model to optimize medical data using the PSO algorithm

Abstract

The problems of high-dimensionality of medical data are considered the most important thing in any dataset analysis,4cially that which specializes in high-dimensional medical data, where you cannot determine which feature falls among the thousands of features that are located on each chromosome and in each of them are thousands of genes. Reducing the dimensions of high-dimensional medical data is an important matter because it has a great impact on the early diagnosis of many diseases, including all kinds of cancer, and most importantly all kinds of cancer. Since we have not found a guaranteed treatment for him to this day, such studies that contribute to the service of the medical field will have adult approval in society. The particle swarm optimization (PSO) algorithm is used for this purpose by evolutionary algorithms. Obtaining good results by using this algorithm, which is considered one of the important evolutionary algorithms due to the importance of its results through the news of a model using the (PSO).