|
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter
Copyright Infringement Claims
Indexing Partner
|
Published Paper Details
|
|
| Paper Title: | Breast Cancer Detection and Prediction using Machine Learning |
| Authors Name: | Gemechu Keneni , Raghavendra R |
| Unique Id: | IJSDR2105051 |
| Published In: | Volume 6 Issue 5, May-2021 |
| Abstract: | 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. |
| Keywords: | Breast Cancer, random forest, logistic regression, decision tree, benign, malignant, |
| Cite Article: | "Breast Cancer Detection and Prediction using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.6, Issue 5, page no.316 - 320, May-2021, Available :http://www.ijsdr.org/papers/IJSDR2105051.pdf |
| Downloads: | 000337215 |
| Publication Details: | Published Paper ID: IJSDR2105051 Registration ID:193341 Published In: Volume 6 Issue 5, May-2021 DOI (Digital Object Identifier): Page No: 316 - 320 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijsdr.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
Track Paper
Important Links
Conference Proposal
ISSN
 |
 |
DOI (A digital object identifier)
  Providing A digital object identifier by DOI How to GET DOI and Hard Copy Related |
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
 |
 |
 |
Social Media
Indexing Partner |
|||
| Copyright © 2024 - All Rights Reserved - IJSDR | |||
Facebook Twitter Instagram LinkedIn