Development of Prediction Model for 5-year Survival of Colorectal Cancer.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2024-09-04 eCollection Date: 2024-01-01 DOI:10.1177/11769351241275889

Raoof Nopour

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Abstract

Objectives: This study aims to introduce a prediction model based on a machine learning approach as an efficient solution for prediction purposes to better prognosis and increase CRC survival.

Methods: In the current retrospective study, we used the data of 1062 CRC cases to analyse and establish a prediction model for the 5-year CRC survival. The machine learning algorithms were used to develop prediction models, including random Forest, XG-Boost, bagging, logistic regression, support vector machine, artificial neural network, decision tree, and K-nearest neighbours.

Results: The current study revealed that the XG-Boost with AU-ROC of 0.906 and 0.813 for internal and external conditions gave us better insight into predictability and generalizability than other algorithms.

Conclusion: XG-Boost can be utilised as a knowledge source for implementing intelligent systems as an assistive tool for clinical decision-making in healthcare settings to improve prognosis and increase CRC survival through various clinical solutions that doctors can achieve.

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开发结直肠癌 5 年生存率预测模型

研究目的本研究旨在引入一种基于机器学习方法的预测模型，作为一种有效的预测解决方案，以改善预后并提高 CRC 的存活率：在本次回顾性研究中，我们使用了 1062 例 CRC 病例的数据，分析并建立了 CRC 5 年生存率预测模型。方法：在本次回顾性研究中，我们利用 1062 例 CRC 病例数据分析并建立了 CRC 5 年生存率预测模型，其中包括随机森林（random Forest）、XG-Boost、bagging、逻辑回归、支持向量机、人工神经网络、决策树和 K-nearest neighbours 等机器学习算法：目前的研究显示，XG-Boost 在内部和外部条件下的 AU-ROC 分别为 0.906 和 0.813，与其他算法相比，XG-Boost 能更好地洞察可预测性和可推广性：XG-Boost可以作为一种知识源，用于实施智能系统，作为医疗机构临床决策的辅助工具，通过医生可以实现的各种临床解决方案，改善预后，提高CRC的存活率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.