Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

The difficulty in predicting early cancer is due to the lack of illness indicators. Metaheuristic approaches are a family algorithms that seek find optimal values for uncertain problems with several implications optimization and classification problems. An automated system recognizing illnesses can respond accuracy, efficiency, speed, helping medical professionals spot abnormalities lowering death rates. This study proposes Novel Hybrid GAO (Genetic Arithmetic Optimization algorithm based Feature Selection) Algorithm-based feature selection) method as way choose features machine learning classify readily available data on COVID-19 lung cancer. By choosing just important features, selection might improve performance. proposed approach employs Genetic enhance outcomes an approach.