Collation of Machine Learning Techniques to Predict the Spread of Breast Cancer
Keywords:
Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB) and Machine Learning (ML)Abstract
Purpose: The purpose of this research study is to compare different machine learning approaches used in breast cancer prediction by using mammography data. By making such strides, our research significantly advances ongoing attempts to enhance breast cancer prediction, enable earlier diagnosis, and perhaps save lives.
Design: With the potential to greatly enhance breast cancer diagnosis and prediction, machine learning techniques have become more important tools in predictive analysis. Through the exploration of various datasets, researchers want to employ various algorithms to predict the incidence of breast cancer, thus supporting the continuous endeavors to augment prediction precision and ameliorate patient consequences. The selected methodology entails a thorough analysis of datasets obtained from prior research, offering a solid basis for the thorough assessment of the precision and dependability of various machine learning algorithms.
Findings: These methods make use of a wide range of information, such as minute data regarding the size and features of the tumor, to be extremely important in the early detection of breast cancer.
Originality: With this information at their disposal, medical professionals may make better decisions and help those who are at risk of breast cancer receive timely interventions. In the field of breast cancer prediction, this comparison analysis is highly significant since it aims to determine the best strategy for improving the precision of early detection techniques.
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