Authors :
Inam, Ul Haq; M. Mudasar Azeem; Mubasher Hussain; Shazab Bashir
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4u2kcvwe
DOI :
https://doi.org/10.5281/zenodo.8285931
Abstract :
Breast cancer is considered one of the biggest
killers in women globally. The major reason of mortality
is the reason that cancer is diagnosed at later stages.
Objectively, this study is conducted to compare evaluation
metrics of 6 ML models such as Naïve Bayes, k-Nearest
Neighborhood (K-NN’s), Decision Tree (DT), Random
Forest (RF), Support Vector Machine (SVM) and Logistic
Regression (LR) on Wisconsin Breast Cancer (BC)
Dataset. WEKA tool has been used to calculate the
performance evaluation of these supervised ML
algorithms. The literature shows that the Weka tool has
been widely used in various data mining problems. The
results clearly show that two models have achieved better
accuracy, recall and other performance metrics in order
to identify risk of breast cancer in women. These two
models are K-NNs and Random Forest. In conclusion,
these supervised classifiers have been trained to detect
malignant and benign cells. In the future, this study may
be extended for BC classification on medical images on
larger dataset in order to diagnose cancer at early stages.
Keywords :
Machine Learning Algorithms, Breast Cancer, WEKA, ML Classifiers.
Breast cancer is considered one of the biggest
killers in women globally. The major reason of mortality
is the reason that cancer is diagnosed at later stages.
Objectively, this study is conducted to compare evaluation
metrics of 6 ML models such as Naïve Bayes, k-Nearest
Neighborhood (K-NN’s), Decision Tree (DT), Random
Forest (RF), Support Vector Machine (SVM) and Logistic
Regression (LR) on Wisconsin Breast Cancer (BC)
Dataset. WEKA tool has been used to calculate the
performance evaluation of these supervised ML
algorithms. The literature shows that the Weka tool has
been widely used in various data mining problems. The
results clearly show that two models have achieved better
accuracy, recall and other performance metrics in order
to identify risk of breast cancer in women. These two
models are K-NNs and Random Forest. In conclusion,
these supervised classifiers have been trained to detect
malignant and benign cells. In the future, this study may
be extended for BC classification on medical images on
larger dataset in order to diagnose cancer at early stages.
Keywords :
Machine Learning Algorithms, Breast Cancer, WEKA, ML Classifiers.
