Comparative Analysis on Medical Image Prediction of Breast Cancer Disease using Various Machine Learning Algorithms

Breast Cancer disease is the utmost characterized heterogeneous illnesses consisting of various types. Apart from lung cancer, Breast cancer is spreading widely everywhere. This research work confines to accurately analyzing the benign cells and the defective malignant cells by data mining technique...

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Bibliographic Details
Published in2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) pp. 1522 - 1526
Main Authors Gurusamy, Ravikumar, Rajmohan, V., Sengottaiyan, N, Kalyanasundaram, P., Ramesh, S.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2023
Subjects
Benign
Breast cancer
Communication systems
Error analysis
Lung cancer
Machine learning
Machine learning algorithms
Malignant
Novel SVM
Support vector machines
Wisconsin Dataset
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Summary:Breast Cancer disease is the utmost characterized heterogeneous illnesses consisting of various types. Apart from lung cancer, Breast cancer is spreading widely everywhere. This research work confines to accurately analyzing the benign cells and the defective malignant cells by data mining technique like Support Vector Machine (SVM). To have the comparative study, a total number of 659 sample are drawn from the UCI Machine learning laboratory. The G power calculation with a confidence interval of 0.8 using maximum level of acceptable error rate of 0.5 is used for this analysis. Support Vector Machine offer better prediction in terms of F1 score, precision and recall as 100%, 92%, 97% for benign cells 94%, 100%, 97% for malignant cells respectively. The significance value is arrived as 0.36 for this proposed system. The SVM appears to have better results in finding the benign and malignant cells diagnosis using Wisconsin Dataset.
DOI:10.1109/ICESC57686.2023.10193635