Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk

1. Dept. of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran 2. Dept. of Health Information Technology, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran 3. Dept. of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran 4. Dept. of Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran

[1]  S. Shahidsales,et al.  Survival Prediction in Patients with Colorectal Cancer Using Artificial Neural Network and Cox Regression , 2020 .

[2]  Hong Qiao,et al.  Comparing data mining methods with logistic regression in childhood obesity prediction , 2009, Inf. Syst. Frontiers.

[3]  A. Almasi-Hashiani,et al.  Associations of demographic, socioeconomic, self-rated health, and metastasis in colorectal cancer in Iran , 2019, Medical journal of the Islamic Republic of Iran.

[4]  Saeedeh Pourahmad,et al.  Colorectal Cancer Staging Using Three Clustering Methods Based on Preoperative Clinical Findings. , 2016, Asian Pacific journal of cancer prevention : APJCP.

[5]  Dongmei Ai,et al.  Using Decision Tree Aggregation with Random Forest Model to Identify Gut Microbes Associated with Colorectal Cancer , 2019, Genes.

[6]  Yan Yan,et al.  Potential role of LINC00996 in colorectal cancer: a study based on data mining and bioinformatics , 2018, OncoTargets and therapy.

[7]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[8]  Massoud Saidijam,et al.  Application of Artificial Neural Network in miRNA Biomarker Selection and Precise Diagnosis of Colorectal Cancer , 2019, Iranian biomedical journal.

[9]  B. Levin,et al.  Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer , 2017, PloS one.

[10]  Usha Chouhan,et al.  Comparative Analysis of Machine Learning Algorithms for Mycobacterium Tuberculosis Protein Sequences on the Basis of Physicochemical Parameters , 2014 .

[11]  Pathologic Factors Affecting Colorectal Cancer Survival in a Jamaican Population—the UHWI Experience , 2019, Journal of Racial and Ethnic Health Disparities.

[12]  Subhendu Kumar Pani,et al.  Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset , 2016 .

[13]  Reza Safdari,et al.  Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer , 2018, Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH.

[14]  M. Ghayour-Mobarhan,et al.  Colorectal cancer risk factors in north-eastern Iran: A retrospective cross-sectional study based on geographical information systems, spatial autocorrelation and regression analysis. , 2019, Geospatial health.

[15]  Tong Wu,et al.  Diagnosis of colorectal cancer by near-infrared optical fiber spectroscopy and random forest. , 2015, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[16]  Anna K Rieger,et al.  A Bayesian scoring rule on clustered event data for familial risk assessment – An example from colorectal cancer screening , 2018, Biometrical journal. Biometrische Zeitschrift.

[17]  W. Lu,et al.  FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms , 2020, Cancer medicine.

[18]  Rowena Chau,et al.  Determining the familial risk distribution of colorectal cancer: a data mining approach , 2015, Familial Cancer.

[19]  Mohammad Rahmati,et al.  Exploiting Experts’ Knowledge for Structure Learning of Bayesian Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Chintan Shah,et al.  Comparison of data mining classification algorithms for breast cancer prediction , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[21]  L. Yao,et al.  Diagnostic accuracy of different computer-aided diagnostic systems for malignant and benign thyroid nodules classification in ultrasound images , 2019, Medicine.

[22]  N. Glasgow,et al.  Relative importance of clinical and sociodemographic factors in association with post-operative in-hospital deaths in colorectal cancer patients in New South Wales: An artificial neural network approach. , 2019, Journal of evaluation in clinical practice.

[23]  M. Zali,et al.  Comparison of Basic and Ensemble Data Mining Methods in Predicting 5-Year Survival of Colorectal Cancer Patients , 2017, Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH.

[24]  E. Pignoli,et al.  Early detection of colorectal adenocarcinoma: a clinical decision support tool based on plasma porphyrin accumulation and risk factors , 2018, BMC cancer.

[25]  Mohammad Rahmati,et al.  Exploiting Experts’ Knowledge for Structure Learning of Bayesian Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Mark Hoogendoorn,et al.  Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records , 2016, Comput. Biol. Medicine.

[27]  E. Giovannucci,et al.  Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies , 2019, Nature Reviews Gastroenterology & Hepatology.

[28]  A. Jemal,et al.  Colorectal cancer statistics, 2020 , 2020, CA: a cancer journal for clinicians.

[29]  Y. Wang,et al.  Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis. , 2016, Genetics and molecular research : GMR.

[30]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[31]  Ping Yang,et al.  Establishment of multiple diagnosis models for colorectal cancer with artificial neural networks , 2019, Oncology letters.

[32]  Felix C. H. Chan,et al.  Predicted Increases in Incidence of Colorectal Cancer in Developed and Developing Regions, in Association With Ageing Populations , 2017, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[33]  Gregory R. Hart,et al.  Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data , 2019, PloS one.

[34]  Tole Sutikno,et al.  Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases , 2015 .

[35]  M. Hueman,et al.  Colorectal Cancer Surveillance: What Is the Optimal Frequency of Follow-up and Which Tools Best Predict Recurrence? , 2017, Current Colorectal Cancer Reports.