Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

Fri Apr 19 00:09:59 2024

(2022) Comparing Data Mining Algorithms for Breast Cancer Diagnosis. Shiraz E Medical Journal. ISSN 17351391 (ISSN)

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Background: Early screening and diagnosis of breast cancer (BC) is critical for improving the quality of care and reducing the mortality rate. Objectives: This study aimed to construct and compare the performance of several machine learning (ML) algorithms in predicting BC. Methods: This descriptive and applied study included 1,052 samples (442 BC and 710 non-BC) with 30 features related to positive and negative BC diagnoses. The data mining (DM) process was implemented using the selected algorithm, including J-48 and random forest (RF) decision tree (DT), multilayer perceptron (MLP), Naïve Bayes (NB), Adaboost (AB), and logistics regression (LR) classifier. Then, we obtained the best algorithm by comparing their performances using the confusion matrix and area under the receiver operator characteristics (ROC) curve (AUC). Finally, we adopted the best model for BC prognosis. Results: The results of evaluating various DM algorithms revealed that the J-48 DT algorithm had the best performance (AUC = 0.922), followed by the AB, MLP, LR, and RF algorithms (AUC: 0.899, 0819, 0.716, and 0.703, respectively). Also, the NB algorithm achieved the lowest performance in this regard (AUC = 0.669). Conclusions: The ML presents a reasonable level of accuracy for an early diagnosis and screening of breast malignancies. Also, the empirical results showed that the J-48 DT algorithm yielded higher performance than other classifiers. © 2022, Author(s).

Item Type: Article
Creators:
CreatorsEmail
Shanbehzadeh, M.UNSPECIFIED
Nopour, R.UNSPECIFIED
Mashoufi, M.UNSPECIFIED
Erfannia, L.UNSPECIFIED
Amraei, M.UNSPECIFIED
Mehrabi, N.UNSPECIFIED
Keywords: Artificial Intelligence Breast Cancer Data Mining Decision Tree Machine Learning oil adaboost adult alcohol consumption algorithm area under the curve Article Bayesian learning body mass cancer diagnosis cancer prognosis cancer radiotherapy classifier colorectal cancer common cold diabetes mellitus diagnostic test accuracy study early diagnosis fast food food fruit consumption human hypercholesterolemia hypertension logistics regression mortality rate multilayer perceptron physical activity prediction random forest receiver operating characteristic retrospective study salt intake sensitivity and specificity vegetable consumption waist hip ratio walking
Divisions:
Journal or Publication Title: Shiraz E Medical Journal
Journal Index: Scopus
Volume: 23
Number: 7
Identification Number: https://doi.org/10.5812/semj-120140
ISSN: 17351391 (ISSN)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/4111

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