The Prediction of Peritoneal Carcinomatosis in Patients with Colorectal Cancer Using Machine Learning (original) (raw)
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Scientific Reports, 2020
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COMPUTATIONAL INTELLIGENCE SYSTEM FOR LOWER THE RISK OF COLORECTAL CANCER IN HIGH DIMENSIONAL DATA
IAEME PUBLICATION, 2021
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SN Computer Science, 2023
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Digestive Diseases and Sciences
Background Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. Aims To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Methods Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at C1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a ''calendar year'' based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Results Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. Conclusions ColonFlag Ò identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180-360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers. Keywords Colorectal neoplasms Á Colonoscopy Á Medical informatics computing Á Blood cell count Á Hemoglobin Á Area under receiver operating characteristics curve Background and Aims An estimated 134,492 new cases of colorectal cancer (CRC), evenly distributed among men and women, were diagnosed in 2016 in the USA, and 49,190 persons died from CRC in 2016-26,020 males and 23,170 females [1].
Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk
Zanjan University of Medical Sciences, 2021
Article Info ABSTRACT 10.30699/jambs.29.133.100 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.