Abstract
Medical data classification is an important factor in improving diagnosis and treatment and can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (FWA), are proposed for enhancing the classification accuracy of the Artificial Neural Network (ANN). The enhancement process is tested through two experiments. First, the proposed algorithms are applied on five benchmark medical data sets from the repository of the University of California in Irvine (UCI). The model with the best results is then used in the second experiment, which focuses on tuning the parameters of the selected algorithm by choosing a different number of iterations in ANNs with different numbers of hidden layers. Enhanced ANN with the three optimization algorithms are tested on biological gene sequence big dataset obtained from The Cancer Genome Atlas (TCGA) repository. GA and FWA are statistically significant but PSO was statistically not, and GA overcame PSO and FWA in performance. The methodology is successful and registers improvements in every step, as significant results are obtained.
Highlights
Medical data classification is an important factor in enhancing diagnosis and treatment
This study aims to enhance artificial neural network (ANN) by hybridizing it with three optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO), and fireworks algorithm (FWA), which are genetic algorithm (GA), particle swarm optimization (PSO), and fireworks algorithm (FWA), which considered population-base metaheuristic algorithms
This paper investigates the hybridization of ANN with three optimization algorithms, namely, This investigates the hybridization of ANN
Summary
Medical data classification is an important factor in enhancing diagnosis and treatment. This field continues to grow for computer researchers because of the major role played by medical data in human life. Classifying medical data can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. Symptoms of patients are used as attributes for a disease data set, which considers the number of instances. The large amount of available medical data might be useful in healthcare. Data mining can be used in analyses of medical centers for providing sufficient sources, timely detection, and prevention of diseases, and avoiding high expenses caused by undesired and costly medical tests [1].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
