Authors :
P.Lakshmi Sai Saran; P.Hemanth Kumar; Md.Sohail
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/yc8rpc7t
DOI :
https://doi.org/10.5281/zenodo.8210572
Abstract :
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.
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.
