A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system

Abstract

The clinical and diagnostic levels of medical decision-making may benefit from use of machine learning techniques. Algorithms for feature selection provide an basis about machine learning. The most discriminating health-related traits from the initial feature set may be quickly and effectively identified in a medical context by using feature selection. Choosing the most relevant attributes of data classes and improving classification performance are the two main goals of feature selection algorithms. The process of feature selection not only identifies the most useful characteristics but also aids in lowering the dataset's overall dimensions. Hence in this article, we propose a novel algorithm based on machine learning. Initially, the dataset is collected and preprocessed using normalisation method. The features are extracted using Linear Discriminant Analysis (LDA) and the relevant features are selected using the proposed Swarm Optimized Clustering based Genetic Algorithm (SOC-GA). On the healthcare datasets, we implemented the suggested method into action. Using Random Forest (RF) and Support Vector Machine (SVM) classifiers, performances of chosen feature subsets are assessed based on accuracy. The empirical findings from our studies in this research are competitive in terms of accuracy and outperformed the other well-known feature selection methods. This research offers remedies that might improve the accurate, efficient and trustworthy decision-making process in healthcare systems for targeted medical treatments.