Performance Evaluation of Wrapper-Based Feature Selection Techniques for Medical Datasets

Developing a good prediction model from excessive-dimensional data in machine intelligence is a difficult endeavor. Attribute subset collection is a crucial step in data preprocessing to find out the best attribute group for improving the efficiency of the directed learning methods. Attribute reduction approaches has been used in many research fields, which deals with the intelligent machine and its capability to learning and giving quick, economical and greater correct for classifying methods. Selecting the small attribute set is a crucial step formaking an intelligent system. The current study has shown that meaningful knowledge can be discovered by applying Machine Learning techniques on medical dataset for disease diagnosis. But due to high dimensional of medical data, there are big computational challenges in this field. If the staging data have inappropriate characteristics, it may degrade classifier accuracy and less comprehensible outcomes. And here, the role of attribute choice plays a crucial role. In this study, we applied a wrapper-based strategy with Particle Swarm Optimization, Genetic Algorithm, and Greedy Hill Climbing as an attribute-oriented approach for medical dataset. The efficiency of the PSO search method is evaluated with the Genetic search method and the Greedy search algorithm. We applied our proposed approach to the three medical databases. The outcomes showed that Particle Swarm Optimization method and Genetic Search method can improve the classification performance and outperformed over the Greedy feature selection technique.