A Comparative Exploration to Perceive Breast Cancer in Mammograms using Machine Learning Algorithms
T.Leena Prema Kumari
, Dr.K.Perumal
Random Forest, Decision Tree, Support Vector Machine, Gradient Boosted Tree, Naïve Bayes, Confusion Matrix.
Breast cancer is the most spreading cancer in women and cause death. Early detection or prediction of cancer can be curable and reduce the mortality rate. There are various Machine Learning algorithms to available to find out the purpose and diagnosis Breast Cancer data. In this various Machine Learning algorithm such as Random Forest, Decision Tree, Support Vector Machine, Gradient Boosted Tree and Naïve Bayes were compared for classifying the Breast Cancer dataset. The data set from Kaggle Machine Learning dataset repository was taken. The results of the classification that get from RF, NB, DT, SVM, GBT were compared. The Performance of each technique is evaluated using Performance metrics such as accuracy, sensitivity, recall and precision. The classification result shows that Random forest have the better accuracy as (98.25%) when compare with other algorithms.
"A Comparative Exploration to Perceive Breast Cancer in Mammograms using Machine Learning Algorithms", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 5, page no.629 - 634, May-2020, Available :https://ijsdr.org/papers/IJSDR2005103.pdf
Volume 5
Issue 5,
May-2020
Pages : 629 - 634
Paper Reg. ID: IJSDR_191847
Published Paper Id: IJSDR2005103
Downloads: 000347195
Research Area: Engineering
Country: MADURAI, TAMILNADU, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
