Authors :
Sirisha Yamani; Ziaul Haque Choudhury
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4n26sz24
DOI :
https://doi.org/10.5281/zenodo.8207264
Abstract :
Worldwide, breast cancer is the leading cause
of death among women. Early detection is essential for
reducing aggressive treatments and increasing survival
rates. Machine learning algorithms have demonstrated
their ability to diagnose breast cancer accurately from
medical imaging data. However, no individual algorithm
can consistently provide optimal results. To address this,
researchers have proposed hybrid ensemble learning
models that combine multiple approaches. In this study,
we have proposed a hybrid ensemble learning model that
combines three powerful algorithms, the Random Forest
(RF), Multilayer Perceptron (MLP) and the Deep Belief
Network (DBN) to diagnose breast cancer accurately.The
MLP and DBN algorithm teaches non-linear correlations
between features and labels, while the RF algorithm uses
a random subset of features to create multiple decision
trees and combine their predictions. The proposed hybrid
model trains the RF, MLP and DBN models separately on
a breast cancer dataset and integrates them using a
weighted average method for the final prediction.Cross-
validation is used to establish the optimal weights for the
RF, MLP and DBN models.Our findings suggest that the hybrid ensemble learning
model is a more reliable and accurate tool for breast
cancer identification than individual machine learning
algorithms. This model has significant potential for early
breast cancer identification in clinical settings, leading to
better patient outcomes and reduced healthcare costs.
Our research demonstrates the effectiveness of hybrid
ensemble learning models in improving breast cancer
identification accuracy.
Keywords :
Breast Cancer, Machine Learning, Random Forest, MLP, DBN, Ensemble Learning, Hybrid Model.
Worldwide, breast cancer is the leading cause
of death among women. Early detection is essential for
reducing aggressive treatments and increasing survival
rates. Machine learning algorithms have demonstrated
their ability to diagnose breast cancer accurately from
medical imaging data. However, no individual algorithm
can consistently provide optimal results. To address this,
researchers have proposed hybrid ensemble learning
models that combine multiple approaches. In this study,
we have proposed a hybrid ensemble learning model that
combines three powerful algorithms, the Random Forest
(RF), Multilayer Perceptron (MLP) and the Deep Belief
Network (DBN) to diagnose breast cancer accurately.The
MLP and DBN algorithm teaches non-linear correlations
between features and labels, while the RF algorithm uses
a random subset of features to create multiple decision
trees and combine their predictions. The proposed hybrid
model trains the RF, MLP and DBN models separately on
a breast cancer dataset and integrates them using a
weighted average method for the final prediction.Cross-
validation is used to establish the optimal weights for the
RF, MLP and DBN models.Our findings suggest that the hybrid ensemble learning
model is a more reliable and accurate tool for breast
cancer identification than individual machine learning
algorithms. This model has significant potential for early
breast cancer identification in clinical settings, leading to
better patient outcomes and reduced healthcare costs.
Our research demonstrates the effectiveness of hybrid
ensemble learning models in improving breast cancer
identification accuracy.
Keywords :
Breast Cancer, Machine Learning, Random Forest, MLP, DBN, Ensemble Learning, Hybrid Model.
