School of Computer Science and Engineering, Central South University, Changsha 410083, China.
"Mobile Health" Ministry of Education-China Mobile Joint Laboratory, Changsha 410083, China.
Comput Math Methods Med. 2020 May 18;2020:6509596. doi: 10.1155/2020/6509596. eCollection 2020.
Prostate cancer (PCa) is one of the main diseases that endanger men's health worldwide. In developing countries, due to the large number of patients and the lack of medical resources, there is a big conflict between doctors and patients. To solve this problem, an auxiliary medical decision system for prostate cancer was constructed. The system used six relevant tumor markers as the input features and employed classical machine learning models (support vector machine and artificial neural network). Stacking method aimed at different ensemble models together was used for the reduction of overfitting. 1,933,535 patient information items had been collected from three first-class hospitals in the past five years to train the model. The result showed that the auxiliary medical system could make use of massive data. Its performance is continuously improved as the amount of data increases. Based on the system and collected data, statistics on the incidence of prostate cancer in the past five years were carried out. In the end, influence of diet habit and genetic inheritance for prostate cancer was analyzed. Results revealed the increasing prevalence of PCa and great negative impact caused by high-fat diet and genetic inheritance.
前列腺癌（PCa）是威胁全球男性健康的主要疾病之一。在发展中国家，由于患者数量多，医疗资源匮乏，医患之间存在很大的矛盾。为了解决这个问题，构建了一个前列腺癌辅助医疗决策系统。该系统使用六个相关肿瘤标志物作为输入特征，并采用经典的机器学习模型（支持向量机和人工神经网络）。堆叠方法旨在将不同的集成模型组合在一起，以减少过拟合。过去五年中，从三家一流医院收集了 1933535 名患者的信息来训练模型。结果表明，辅助医疗系统可以利用大量数据。随着数据量的增加，其性能不断提高。基于该系统和收集的数据，对过去五年前列腺癌的发病率进行了统计。最后，分析了饮食习惯和遗传因素对前列腺癌的影响。结果表明，PCa 的发病率呈上升趋势，高脂肪饮食和遗传因素对 PCa 造成了巨大的负面影响。
