Paper Contents
Abstract
Cancer diagnostic categorization systems are built using machine learning (ML) approaches. These machine learning techniques can assist professionals and novices alike in mitigating potential errors and enable accurate and timely inspection of healthcare databases. However, useful machine learning categorization is limited by the amount of the data. Numerous cancer forecast models have been developed, and they use machine learning approaches to anticipate the onset of the disease and identify its distinguishing features. Experts in health management are concentrating on creating new algorithms and modifying machine learning to make it easier to categorise patient data and analyse it to produce accurate, dependable, and error-free predictions. For this, the ML platform makes use of numerous parameters.
Copyright
Copyright © 2024 Rajiv Kumar. This is an open access article distributed under the Creative Commons Attribution License.
