Analysis of Breast Cancer Survivability Using Machine Learning Predictive Technique for Post-Surgical Patients

Abstract

The primary objective of this study is to predict the likelihood of long-term survival for breast cancer patients who have received surgical treatment for a duration of five years or more. The aim is to provide healthcare providers with accurate predictions that can guide treatment plans and medication decisions. Despite existing breast cancer survivability prediction techniques, their accuracy remains low, limiting their practical utility. Additionally, there is a lack of research specifically addressing the survivability prediction of breast cancer post- surgery. Therefore, this study proposes a deep learning-based approach to predict survivability in this context. The effectiveness of the proposed model in predicting survival rates is evaluated using Haberman's survival dataset, obtained from the University of Chicago's Billings. Different evaluation measures, including accuracy, sensitivity, and specificity, are employed to assess the model's performance. Experimental results demonstrate that the proposed approach outperforms other models, achieving an accuracy of 83.18%, sensitivity of 85.54%, and specificity of 97.19%. The high accuracy of the proposed approach makes it suitable for use by healthcare professionals in predicting breast cancer survivability outcomes. It enables physicians to adjust treatments based on individual patient predictions. Consequently, the suggested method is advisable for practical implementation in systems designed to predict the survival chances of breast cancer patients after undergoing treatment.