A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models
Shler Farhad Khorshid, Adnan Mohsin Abdulazeez, Amira Bibo Sallow
Asian Journal of Research in Computer Science · pp. 45–59 · Published 19 May 2021
10.9734/ajrcos/2021/v8i430209Abstract
Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.
Cited by 23
Gurinder Singh, Prateek Chaturvedi, Anurag Shrivastava · 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) · 2022
S. Nathiya, J. Sumitha · 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) · 2021
Revella E. A. Armya, A. Abdulazeez, A. Sallow · Asian Journal of Research in Computer Science · 2021
M. Mesran, Muhammad Syahrizal, S. Sarwandi · 4TH INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN MATERIALS SCIENCE AND ENGINEERING 2022 · 2024
Bina Estherly, Budi Prasetiyo · INTERNATIONAL CONFERENCE ON APPLIED COMPUTATIONAL INTELLIGENCE AND ANALYTICS (ACIA-2022) · 2023
Wishal Arshad, Tehreem Masood, Tariq Mahmood · IEEE Access · 2023
N. Patel, Manoranajan Panda · 2022
Shweta Kharya, Sunita Soni, Tripti Swarnkar · Advanced Machine Learning for Complex Medical Data Analysis · 2025
Zahra Zul Hulaifah Al Abrori, Egia Rosi Subhiyakto · Jurnal Algoritma · 2025
Shubpreet Kaur, Nishi Nishi, Yashandeep Kaur · 2021 2nd International Conference on Computational Methods in Science & Technology (ICCMST) · 2021
Related research
- Reproductive Factors Associated with the Risk of Breast Cancer among Malaysian Women: A Multi-Centre Case-Control Study — shares topic coverage
- Factors Predicting the Utilization of Breast Cancer Screening Services among Women Working in a Private University in Ogun State, Nigeria — shares topic coverage
- Breast Cancer Screening Trend Year 2017 among South-Western Nigeria Female Residents — shares topic coverage
- Complementary Medicine Intervention in Breast Cancer Patients with Pain — shares topic coverage
- Breast Cancer Knowledge and Mammography Uptake among Women Aged 40 Years and Above in Calabar Municipality, Nigeria — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
23
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.