Faculty of Mathematical and Physical Sciences, University College London, London, WC1E 6BT, UK.
School of Clinical Medicine, Tsinghua University, Beijing 100084, China.
Math Biosci Eng. 2023 Jan 11;20(3):5316-5332. doi: 10.3934/mbe.2023246.
Liver cancer is a common cause of death from cancer in the population, with the 4th highest mortality rate from cancer worldwide. The high recurrence rate of hepatocellular carcinoma after surgery is an important cause of high mortality among patients. In this paper, based on eight scheduled core markers of liver cancer, an improved feature screening algorithm was proposed based on the analysis of the basic principles of the random forest algorithm, and the system was finally applied to liver cancer prognosis prediction to improve the prediction of biomarkers for liver cancer recurrence, and the impact of different algorithmic strategies on the prediction accuracy was compared and analyzed. The results showed that the improved feature screening algorithm was able to reduce the feature set by about 50% while ensuring that the prediction accuracy was reduced within 2%.
肝癌是全球癌症死亡率第 4 高的癌症，也是人群中癌症死亡的常见原因。肝癌手术后的高复发率是导致患者死亡率高的重要原因。本文基于肝癌的 8 个预定核心标志物，在分析随机森林算法基本原理的基础上，提出了一种改进的特征筛选算法，并最终将该系统应用于肝癌预后预测，以提高肝癌复发的生物标志物预测，比较和分析了不同算法策略对预测准确性的影响。结果表明，改进的特征筛选算法在保证预测精度降低不超过 2%的情况下，能够将特征集减少约 50%。
