Breast Cancer Survival Prediction using Machine Learning

Authors : P.Lakshmi Sai Saran; P.Hemanth Kumar; Md.Sohail

Volume/Issue : Volume 8 - 2023, Issue 7 - July

Google Scholar :

Scribd :


Breast cancer is still a major worldwide health issue, highlighting the demand for accurate prognostic tools to support individualised treatment choices. In this article, we describe a unique method for reliably predicting breast cancer survival outcomes that synergistically combines multimodal biomarkers with state-of-the-art machine learning approaches. This study makes use of a large dataset that includes clinical, histological, genetic, and imaging data collected from a heterogeneous group of breast cancer patients. We use sophisticated feature engineering techniques to extract relevant data form each modality and assure robust depiction of the fundamental biological processes by utilising this wealth of data. We investigate a broad spectrum of cutting-edge machine learning algorithms, such as ensemble approaches, deep learning architectures, and explainable AI models, in order to improve model performance and improve interpretability. We determine the best algorithmic framework that maximises predicted accuracy while offering valuable insights into the underlying causes of survival differences through rigorous cross-validation and model selection approaches. Furthermore, in order to pinpoint the most useful biomarkers influencing prognosis, we examine the effects of various feature selection strategies and dimensionality reduction techniques. As a result, it is possible to identify prospective therapeutic targets and create individualised treatment plans.On a sizable and diverse breast cancer dataset, numerous experiments are carried out to verify the efficacy of our suggested architecture. The results show much higher precision, specificity, and sensitivity than those of existing prognostic models, demonstrating superior predictive ability. Additionally, extensive internal and external verification processes have proven that our model achieves great stability and generalizability.

Keywords : Breast Cancer, Machine Learning, AI Models, Prognostic Tools, Extensive Internal and External Verification Processes.


Paper Submission Last Date
30 - November - 2023

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.