An Efficient Novel Approach on WBCD for Early Detection of Breast Cancer using Distributed Machine Learning Techniques
Keywords:
Wisconsin breast cancer dataset, naive Bayesian, logistic regression, k-nearest Neighbors, Independent Component Analysis.Abstract
Breast cancer(BC) will be the most common malignancy in women by 2022,with over 3 million new cases. Due to alterations in risk factor profiles, enhanced cancer registries, and quicker detection over the past three decades, both the incidence and death rates of cancer have risen. The total risk factors for BC are largely made up of modifiable and immutable risk factors. Now, 80 percent of BC patients are over 50. The molecular subtype and stage affect survival. One of the most important problems affecting people in underdeveloped nations is the death rate from cancer. Even while there are many ways to avoid getting cancer in the first place, some illnesses can still be fatal. Breast cancer is one of the most common types of cancer, and early detection is key to a successful course of therapy. One of the most crucial aspects of breast cancer treatment is a precise diagnosis. Several studies that have been written about in the literature can indicate the type of breast cancer. Using data on breast cancer tumours from Dr. William H. Wahlberg of the Hospital of the University of Wisconsin, the kind of breast tumour was predicted in this study. On this dataset, data visualization and machine learning techniques like Distributed Polynomial Kernel SVM were used. This study compared the identification and diagnosis of breast cancer using data visualization and machine learning methods. The most accurate classification achieved by the Distributed Logistic Regression Model employing all features is improved by the proposed approach. Modern technology has been enhanced with new hybrid frameworks and models for higher security, data storage of large volumes, and precision. Using machine learning classification methods, a function that can forecast the discrete class of new items has also been improved.
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