Breast Cancer Detection and Prediction using Machine Learning
Gemechu Keneni
, Raghavendra R
Breast Cancer, random forest, logistic regression, decision tree, benign, malignant,
One the top type of cancer in women takes twenty-five percent of all cancer death around the globe is breast cancer. Proper and early treatment is the best solution for best diagnosis. Manual diagnostic needs experienced pathologists and much amount of time. Automated technique of detecting breast cancer improves accuracy and saves the specified diagnosis time. Therefore, the aim of this thesis to build up a methodology which enable detecting to maximize the number of breast cancer, identified at infant stage increase effectiveness of the treatment so that to reduce the number of death from breast cancer. Detecting breast is one of the solutions to effective treatment of breast cancer. I use different machine learning algorithm like Logistic regression, decision tree and random forest classifier to forecast if the tumor is not cancer. The proposed techniques were evaluated employing a confusion matrix, and classification performance report back to assess which features a higher classification potential. The logistic regression algorithm has achieved an average accuracy of 95%, average precision of 95.0%, average recall 95.0% and an average F1 value of 95.0% over a test data-set of previously unseen 143.The decision tree algorithm has achieved an average accuracy of 93%, average precision of 93.0%, average recall 93.0% and an average F1 value of 93.0% over a test data-set of previously unseen 143.The random forest classifier algorithm has achieved an average accuracy of 96%, average precision of 96.0%, average recall 96.0% and an average F1 value of 96.0% over a test data-set of previously unseen 143.From the analysis of the experimental results, the random forest algorithm gives better results than the other supervising machine learning classifiers. The accuracy of the model is 96 % so we can see a few wrong predictions but mostly this model is successful in predicting a tumor Malignant (M) (harmful) or Benign (B) (not harmful) based upon the features provided in the data and the training given.
"Breast Cancer Detection and Prediction using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.6, Issue 5, page no.316 - 320, May-2021, Available :https://ijsdr.org/papers/IJSDR2105051.pdf
Volume 6
Issue 5,
May-2021
Pages : 316 - 320
Paper Reg. ID: IJSDR_193341
Published Paper Id: IJSDR2105051
Downloads: 000347268
Research Area: Engineering
Country: Ramanagar, Karnataka, 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
