Enhancing Breast Cancer Prediction Through SVM-Based Analysis

Breast cancer is a significant global health concern among women and ranks as one of the leading causes of concern in healthcare. The probability of effectively treating breast cancer can be significantly improved if it is detected at an early stage. In recent years, it has become increasingly commo...

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Bibliographic Details
Published in:	2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS) pp. 1 - 6
Main Authors:	Kalyanapu, Srinivas, Kandula, Ashok Reddy, Madhuri, Pittu, Ganta, Pujitha, Sai Sri Vattikuti, Hema Naga, Darapu, Unnathi
Format:	Conference Proceeding
Language:	English
Published:	IEEE 16-11-2023
Subjects:	Breast cancer Breast Cancer Prediction Classificaton models Early Detection Strategies gridsearch optimization Machine Learning Medical services Optimizing Hyperparameters Planning Predictive models Prognostics and health management Support Vector Machine(SVM) Support vector machines
Online Access:	Get full text
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Summary:	Breast cancer is a significant global health concern among women and ranks as one of the leading causes of concern in healthcare. The probability of effectively treating breast cancer can be significantly improved if it is detected at an early stage. In recent years, it has become increasingly common to use machine learning techniques for breast cancer prognosis and predicting the likelihood of breast cancer. By utilizing Support Vector Machines (SVM) in the context of this document, we introduce a novel approach for predicting breast cancer, aided by an empirical model. To enhance the predictive power of our SVM-based breast cancer prediction model, we employed a technique called Grid Search. Based on data collected from the Wisconsin Diagnostic Breast Cancer (WDBC) database, Our findings indicate that the proposed SVM model with Grid Search achieves an accuracy rate of 96%, demonstrating its potential as a clinical tool for breast cancer prediction.
DOI:	10.1109/AICERA/ICIS59538.2023.10420106