Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study

Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study Publisher Pubmed

Mohammadi G¹ ; Looha MA² ; Pourhoseingholi MA³ ; Tavirani MR⁴ ; Sohrabi S⁵ ; Khaneh AZS⁶ ; Piri H⁷ ; Alaei T⁸ ; Parvani N⁵ ; Vakilzadeh I⁵ ; Javadi S⁹ ; Cheshmeh ZMH¹⁰ ; Razzaghi Z¹¹ ; Robati RM¹² Show All Authors

Mohammadi G¹
Looha MA²
Pourhoseingholi MA³
Tavirani MR⁴
Sohrabi S⁵
Khaneh AZS⁶
Piri H⁷
Alaei T⁸
Parvani N⁵
Vakilzadeh I⁵
Javadi S⁹
Cheshmeh ZMH¹⁰
Razzaghi Z¹¹
Robati RM¹²
Azodi MZ⁴
Shahraki SZ¹³
Talebi R¹⁴
Yazdani JC¹⁵
Motlagh ME¹⁶
Khodakarim S¹⁷
Hadavi M⁶

Show Affiliations

1. Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2. Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3. Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4. Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
5. Vice Chancellor in Administration and Resources Development Affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran
6. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
7. Shahid Beheshti University of Medical Sciences, Tehran, Iran
8. Cardiovascular Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
9. Vice Chancellor for Research & Technology, Shiraz University of Medical Sciences, Shiraz, Iran
10. Department of Epidemiology, Faculty of Health, Iran University of Medical Science, Tehran, Iran
11. Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
12. Department of Dermatology, Director of Skin Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
13. Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
14. Department of Mathematics at Architecture and Computer Engineering, University of Applied Sciences (unit 10), Tehran, Iran
15. Health Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran
16. Department of Pediatrics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
17. Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Source: Asian Pacific Journal of Cancer Prevention Published:2024

Abstract

Introduction: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths. This study aimed to predict survival outcomes of CRC patients using machine learning (ML) methods. Material and Methods: A retrospective analysis included 1853 CRC patients admitted to three prominent tertiary hospitals in Iran from October 2006 to July 2019. Six ML methods, namely logistic regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean decrease GINI criteria. Model performance was assessed using Area Under the Curve (AUC). Results: Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as crucial predictors of survival based on mean decrease GINI. The NB (AUC = 0.70, 95% Confidence Interval [CI] 0.65–0.75) and LGBM (AUC = 0.70, 95% CI 0.65–0.75) models achieved the highest predictive AUC values for CRC patient survival. Conclusions: This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes. © (2024) This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License.

Experts (# of related papers)

Sadaf Ghajarieh Sepanlou (1)

Akram Pourshams (1)

Style	Citing Format
MLA	Mohammadi G, et al.. "Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study." Asian Pacific Journal of Cancer Prevention, vol. 25, no. 1, 2024, pp. 333-342.
APA	Mohammadi G, Looha MA, Pourhoseingholi MA, Tavirani MR, Sohrabi S, Khaneh AZS, Piri H, Alaei T, Parvani N, Vakilzadeh I, Javadi S, Cheshmeh ZMH, Razzaghi Z, Robati RM, Azodi MZ, Shahraki SZ, Talebi R, Yazdani JC, Motlagh ME, ... Hadavi M (2024). Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study. Asian Pacific Journal of Cancer Prevention, 25(1), 333-342.
Chicago	Mohammadi G, Looha MA, Pourhoseingholi MA, Tavirani MR, Sohrabi S, Khaneh AZS, Piri H, et al.. "Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study." Asian Pacific Journal of Cancer Prevention 25, no. 1 (2024): 333-342.
Harvard	Mohammadi G et al. (2024) 'Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study', Asian Pacific Journal of Cancer Prevention, 25(1), pp. 333-342.
Vancouver	Mohammadi G, Looha MA, Pourhoseingholi MA, Tavirani MR, Sohrabi S, Khaneh AZS, et al.. Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study. Asian Pacific Journal of Cancer Prevention. 2024;25(1):333-342.
BibTex	@article{ author = {Mohammadi G and Looha MA and Pourhoseingholi MA and Tavirani MR and Sohrabi S and Khaneh AZS and Piri H and Alaei T and Parvani N and Vakilzadeh I and Javadi S and Cheshmeh ZMH and Razzaghi Z and Robati RM and Azodi MZ and Shahraki SZ and Talebi R and Yazdani JC and Motlagh ME and Khodakarim S and Hadavi M}, title = {Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study}, journal = {Asian Pacific Journal of Cancer Prevention}, volume = {25}, number = {1}, pages = {333-342}, year = {2024} }
RIS	TY - JOUR AU - Mohammadi G AU - Looha MA AU - Pourhoseingholi MA AU - Tavirani MR AU - Sohrabi S AU - Khaneh AZS AU - Piri H AU - Alaei T AU - Parvani N AU - Vakilzadeh I AU - Javadi S AU - Cheshmeh ZMH AU - Razzaghi Z AU - Robati RM AU - Azodi MZ AU - Shahraki SZ AU - Talebi R AU - Yazdani JC AU - Motlagh ME AU - Khodakarim S AU - Hadavi M TI - Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study JO - Asian Pacific Journal of Cancer Prevention VL - 25 IS - 1 SP - 333 EP - 342 PY - 2024 ER -

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