Authors :
Ali Degirmenci
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3yN0qkK
DOI :
https://doi.org/10.5281/zenodo.7217931
Abstract :
Cervical cancer is one of the most common
vital diseases that still seriously affects women worldwide.
Early detection of it may not be possible due to late onset
of symptoms, community norms, unavailable healthcare
facilities, and medical cost. Computer aided diagnostic
tools have shown very successful results in the early
diagnosis of diseases in recent years. Especially the
developments in computer technology have increased the
success of machine learning-based methods. This study
presents and analyzes 3 different machine learning based
algorithms (k nearest neighbor, support vector machines
(SVM), and random forest) to predict cervical cancer.
Hyperparameter optimization of algorithms is performed
by exhaustive grid search and k-fold cross validation is
used to increase the reliability of the results.
Keywords :
Cervical Cancer; kNN; SVM; Random Forest; Computer Aided Diagnosisinsert.
Cervical cancer is one of the most common
vital diseases that still seriously affects women worldwide.
Early detection of it may not be possible due to late onset
of symptoms, community norms, unavailable healthcare
facilities, and medical cost. Computer aided diagnostic
tools have shown very successful results in the early
diagnosis of diseases in recent years. Especially the
developments in computer technology have increased the
success of machine learning-based methods. This study
presents and analyzes 3 different machine learning based
algorithms (k nearest neighbor, support vector machines
(SVM), and random forest) to predict cervical cancer.
Hyperparameter optimization of algorithms is performed
by exhaustive grid search and k-fold cross validation is
used to increase the reliability of the results.
Keywords :
Cervical Cancer; kNN; SVM; Random Forest; Computer Aided Diagnosisinsert.
