摘要
当前机器学习在医疗诊断领域得到广泛应用。本文基于改进的随机森林算法,利用患者进行各项检查获得的大量数据,对照肝硬化指标,对患者数据进行分析处理,提出一种基于患者检查数据的肝硬化预测方法。该方法改进传统诊断技术,采用随机森林算法,利用其使用随机因子来控制数据多样性的特点,引入深度限制指标,提高算法对数据的判断和识别能力,增强预测的准确性。本文采用人体测量学组成的数据集进行实验,结果表明该方法预测准确率达到90%以上。
Machine learning is widely applied in the field of medical diagnosis currently. Based on the improved random forest algorithm, a prediction method for liver cirrhosis diagnosis is proposed, in which the patients’ data with liver cirrhosis indicators is analyzed and processed by means of the large amount of data obtained by patients for each examination and liver cirrhosis indicators. The method of the paper has improved the traditional diagnosis technology, adopted the random forest algorithm, used its random factor to control the characteristics of data diversity, and introduced the depth limit index. And it has improved the judgment and recognition ability of the data, and enhanced the prediction accuracy. In this paper, the data set composed of anthropometrics is used for experiments. The results show that the prediction accuracy of this method is over 90%.
出处
《计算机科学与应用》
2019年第10期1928-1938,共11页
Computer Science and Application
基金
黑龙江省教育厅基本业务专项理工面上项目(135209234)
齐齐哈尔市基金项目(GYGG-201913).
