Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Hassan Falah Fakhruldeen
Imam Ja’afar Al-sadiq University,Baghdad

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparative evaluation of data mining algorithms in breast cancer Mustafa Qahtan Alsudani; Hassan Falah Fakhruldeen; Israa Fayez Yousif
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp777-784

Abstract

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. The authors of this review studied numerous data mining algorithms and implemented them such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, additive regression (AR), back propagation (BP) neural network, and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the authors found that SVM and Bagging had the highest precision and accuracy, respectively. Also assess the number of studies that provide machine learning techniques for breast cancer detection.
Co-Authors Israa Fayez Yousif Mustafa Qahtan Alsudani