Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.

Aamir, Sanam; Rahim, Aqsa; Aamir, Zain; Abbasi, Saadullah Farooq; Khan, Muhammad Shahbaz; Alhaisoni, Majed; Khan, Muhammad Attique; Khan, Khyber; Ahmad, Jawad

Aamir, Sanam; Rahim, Aqsa; Aamir, Zain; Abbasi, Saadullah Farooq; Khan, Muhammad Shahbaz; Alhaisoni, Majed; Khan, Muhammad Attique; Khan, Khyber; Ahmad, Jawad.

Affiliation

Aamir S; Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.
Rahim A; Faculty of Science and Technology, University of Tromsø, Tromso, Norway.
Aamir Z; Department of Data Science, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
Abbasi SF; Department of Electrical Engineering, National University of Technology, Islamabad 44000, Pakistan.
Khan MS; Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan.
Alhaisoni M; Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Khan MA; Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Khan K; Department of Computer Science, HITEC University, Taxila, Pakistan.
Ahmad J; Department of Computer Science, Khurasan University, Jalalabad, Afghanistan.

Comput Math Methods Med ; 2022: 5869529, 2022.

Article in En | MEDLINE | ID: mdl-36017156

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans Language: En Journal: Comput Math Methods Med Journal subject: INFORMATICA MEDICA Year: 2022 Document type: Article Affiliation country: Pakistán Country of publication: Estados Unidos

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