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Machine learning (ML) plays a key job in the guide of cancer diagnosis and identification. The researcher has implemented different algorithms of ML for the prediction of breast cancer. Some researchers recommend their algorithms are more accurate, faster, and easier than others. My study relies on recently developed machine learning algorithms like genetic algorithms and deep belief nets. I’m interested to build a framework to precisely separate among benign and malignant tumors. We’ve optimized the training algorithm. During this unique circumstance, we applied the genetic algorithm procedure to settle on the main genuine highlights and perfect boundary estimations of the AI classifiers. The examinations rely upon affectability, cross-validation, precision, and ROC curve. Among all the varying kinds of classifiers used in this paper genetic programming is the premier viable model for highlight determination and classifier. ### Competing Interest Statement The authors have declared no competing interest.
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