Comparative analysis in execution of machine learning in breast cancer identification: a review
Carcinoma known as breast cancer is a significant common cancer among women worldwide. In line with the global trends, it accounts for many new cancer cases and cancer-related deaths, giving it a substantial public health issue in today's culture. Early diagnosis is the most effective method to...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31703/ http://umpir.ump.edu.my/id/eprint/31703/1/Comparative%20analysis%20in%20execution%20of%20machine%20learning.pdf |
| _version_ | 1848823835063746560 |
|---|---|
| author | Ravindran, Nadarajan Noorazliza, Sulaiman |
| author_facet | Ravindran, Nadarajan Noorazliza, Sulaiman |
| author_sort | Ravindran, Nadarajan |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Carcinoma known as breast cancer is a significant common cancer among women worldwide. In line with the global trends, it accounts for many new cancer cases and cancer-related deaths, giving it a substantial public health issue in today's culture. Early diagnosis is the most effective method to reduce the number of deaths in patients with breast cancer. Effective and early diagnosis of breast cancer ensure like mammography or biopsy to ensure the long-term survival of affected patients. Several conflicts arise in using traditional approaches, such as overdiagnosis or under-diagnosis. Machine learning is used to overcome the issues where it can strengthen the current conventional diagnosing of patients with breast cancer. The application of the classification method for diagnosing breast cancer is reviewed in this paper. Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbour (KNN), Decision Tree, Artificial Neural Network (ANN), and logistic regression are six methods presented in the review. These techniques are integrated with conventional methods, often allow physicians to diagnose breast cancer effectively. In summary, machine learning improvises in diagnosing breast cancer in terms of accuracy, sensitivity, and specificity with excellent performance and quality of patients. |
| first_indexed | 2025-11-15T03:03:27Z |
| format | Conference or Workshop Item |
| id | ump-31703 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:03:27Z |
| publishDate | 2021 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-317032021-07-26T14:00:24Z http://umpir.ump.edu.my/id/eprint/31703/ Comparative analysis in execution of machine learning in breast cancer identification: a review Ravindran, Nadarajan Noorazliza, Sulaiman TK Electrical engineering. Electronics Nuclear engineering Carcinoma known as breast cancer is a significant common cancer among women worldwide. In line with the global trends, it accounts for many new cancer cases and cancer-related deaths, giving it a substantial public health issue in today's culture. Early diagnosis is the most effective method to reduce the number of deaths in patients with breast cancer. Effective and early diagnosis of breast cancer ensure like mammography or biopsy to ensure the long-term survival of affected patients. Several conflicts arise in using traditional approaches, such as overdiagnosis or under-diagnosis. Machine learning is used to overcome the issues where it can strengthen the current conventional diagnosing of patients with breast cancer. The application of the classification method for diagnosing breast cancer is reviewed in this paper. Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbour (KNN), Decision Tree, Artificial Neural Network (ANN), and logistic regression are six methods presented in the review. These techniques are integrated with conventional methods, often allow physicians to diagnose breast cancer effectively. In summary, machine learning improvises in diagnosing breast cancer in terms of accuracy, sensitivity, and specificity with excellent performance and quality of patients. IOP Publishing 2021-06-15 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/31703/1/Comparative%20analysis%20in%20execution%20of%20machine%20learning.pdf Ravindran, Nadarajan and Noorazliza, Sulaiman (2021) Comparative analysis in execution of machine learning in breast cancer identification: a review. In: Journal of Physics: Conference Series; 1st International Recent Trends in Engineering, Advanced Computing and Technology Conference, RETREAT 2020 , 1 - 3 December 2020 , Paris, France (Virtual). pp. 1-10., 1874 (1). ISSN 1742-6588 (print); 1742-6596 (online) (Published) https://doi.org/10.1088/1742-6596/1874/1/012032 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ravindran, Nadarajan Noorazliza, Sulaiman Comparative analysis in execution of machine learning in breast cancer identification: a review |
| title | Comparative analysis in execution of machine learning in breast cancer identification: a review |
| title_full | Comparative analysis in execution of machine learning in breast cancer identification: a review |
| title_fullStr | Comparative analysis in execution of machine learning in breast cancer identification: a review |
| title_full_unstemmed | Comparative analysis in execution of machine learning in breast cancer identification: a review |
| title_short | Comparative analysis in execution of machine learning in breast cancer identification: a review |
| title_sort | comparative analysis in execution of machine learning in breast cancer identification: a review |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/31703/ http://umpir.ump.edu.my/id/eprint/31703/ http://umpir.ump.edu.my/id/eprint/31703/1/Comparative%20analysis%20in%20execution%20of%20machine%20learning.pdf |