Abstract
Information extraction, information retrieval, and text classification are only a few of the important study areas that fall under the heading of "bio medical text classification." In order to increase understanding of various information extraction opportunities in the field of data mining, this study analyses several text categorization approaches used in practise, their strengths and shortcomings. We have gathered a dataset with a strong emphasis on three categories, including "Thyroid Cancer," "Lung Cancer," and "Colon Cancer." This essay offers an empirical investigation of a classifier. Benchmarks for biomedical text were used to conduct the experiment. We study many metaheuristic algorithms, including genetic algorithms, particle swarm optimization, firefly, cuckoo, and bat algorithms. The suggested multiple classifier system also outperforms ensemble learning, ensemble pruning, and conventional classification algorithms. In the data we use predict the Biomedical text document classification is whether it's Thyroid Cancer, Lung Cancer, Colon Cancer based on the performed basic EDA, text pre-processing, build different models, such as LogisticRegression, DecisiontreeClassification, RandomForest Classification
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