Predicting the Optimal Treatment for Diseases Using the Genetic Method by Develop (PSO) Optimization Technique
DOI:
https://doi.org/10.29304/jqcsm.2024.16.21545Keywords:
Big data, Machine learning, deep learning, Optimization, Prediction, Genetic algorithms.Abstract
Recently, most researchers have been interested in finding appropriate solutions to the spread of diseases, and the data on diseases considered big data, so dealing with big data is difficult in terms of obtaining highly accurate results because of the missing values. To enhance the deals with this type of data, this paper will use artificial intelligence techniques to design a system to predict the optimal treatment for diseases, regardless of disease type with normalization and processing missing data for dataset of diseases . The Particle Swarm Optimization (PSO) algorithm used to find the best solutions since genetic algorithms have characteristics that distinguish them from other algorithms such as mutation and crossover. The 1X method of crossover has been used to develop the PSO algorithm to give the best prediction for the appropriate treatment for these diseases by using the mathematical equations that are explained in 1.3.1. To prove the enhancement of the PSO algorithm after development, the results obtained from the PSO algorithm were compared before and after development by measuring the Fest Fitness Value (accuracy), Error rate, and MSE measurement, the results displayed improvement after development of the algorithm by increased values of accuracy reached to 94% and decreased of Error rate and MSE
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