Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Comparison of four data mining algorithms for predicting colorectal cancer risk

Wed May 22 12:21:27 2024

(2021) Comparison of four data mining algorithms for predicting colorectal cancer risk. Journal of Advances in Medical and Biomedical Research. pp. 100-108. ISSN 26766264 (ISSN)

Full text not available from this repository.

Official URL:


Background & Objective: Colorectal cancer (CRC) is one of the most prevalent malignancies in the world. The early detection of CRC is not only a simple process but also is the key to treatment. Data mining algorithms could be potentially useful in cancer prognosis, diagnosis, and treatment. Therefore, the main focus of this study is to measure the performance of some data mining classifier algorithms in predicting CRC and providing an early warning to the high-risk groups. Materials & Methods: This study was performed on 468 subjects, including 194 CRC patients and 274 non-CRC cases. We used the CRC dataset from Imam Hospital, Sari, Iran. The Chi-square feature selection method was utilized to analyze the risk factors. Next, four popular data mining algorithms were compared in terms of their performance in predicting CRC, and, finally, the best algorithm was identified. Results: The best outcome was obtained by J-48 with F-measure=0.826, receiver operating characteristic (ROC)=0.881, precision=0.826, and sensitivity =0.827. Bayesian net was the second-best performer (F-Measure=0.718, ROC=0.784, precision=0.719, and sensitivity=0.722) followed by random forest (F-Measure=0.705, ROC=0.758, precision=0.719, and sensitivity=0.712). The multilayer perceptron technique had the worst performance (F-Measure=0.702, ROC=0.76, precision=0.701, and sensitivity=0.703). Conclusion: According to the results of this study, J-48 could provide better insights than other proposed prediction models for clinical applications. © 2021, Zanjan University of Medical Sciences and Health Services. All rights reserved.

Item Type: Article
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Classification models Colorectal cancer Data mining Prediction adult Article Bayesian network cancer patient cancer risk cigarette smoking comparative study controlled study female high risk patient human major clinical study male metabolic syndrome X random forest risk factor
Page Range: pp. 100-108
Journal or Publication Title: Journal of Advances in Medical and Biomedical Research
Journal Index: Scopus
Volume: 29
Number: 133
Identification Number:
ISSN: 26766264 (ISSN)
Depositing User: مهندس مهدی شریفی

Actions (login required)

View Item View Item