سه شنبه 13 آذر 1403
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دوره 29، شماره 133 - ( 11-1399 )
جلد 29 شماره 133 صفحات 108-100 |
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk. J Adv Med Biomed Res 2021; 29 (133) :100-108
URL: http://journal.zums.ac.ir/article-1-6082-fa.html
URL: http://journal.zums.ac.ir/article-1-6082-fa.html
Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk. Journal of Advances in Medical and Biomedical Research. 1399; 29 (133) :100-108
چکیده: (142577 مشاهده)
Background and Objective: Colorectal cancer (
Materials and Methods: This study was performed in 468 subjects (194 CRC patients and 274 non-CRC cases). We used the CRC dataset from the Imam Hospital, Sari, Iran. The Chi-square feature selection method was utilized to analyze the risk factors. Then, four popular data mining algorithms were compared based on their performance in predicting CRC, and, finally, the best algorithm was identified.
Results: The best outcome was obtained by J-48 (F-Measure = 0.826, 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). Random-Forest performed the third-best (F-Measure= 0.705, ROC=0.758, precision= 0.719, and sensitivity=0.712). Finally, the MLP technique performed the worst (F-Measure = 0.702, ROC=0.76, precision = 0.701 and sensitivity=0.703).
Conclusion: According to the results, we concluded that the J-48 could provide better insights than other proposed prediction models for clinical applications.
نوع مطالعه: مقاله پژوهشی |
موضوع مقاله:
Life science
دریافت: 1399/4/3 | پذیرش: 1399/4/26 | انتشار: 1399/9/14
دریافت: 1399/4/3 | پذیرش: 1399/4/26 | انتشار: 1399/9/14
فهرست منابع
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