文献类型：会议论文

英文题名：Computer prediction model for health status using classification tree models and Big data digital image

作者：Wang, Jing[1]; Li, Gongli[1]

第一作者：王静

机构：[1] School of Statistics and Big Data, Henan University of Economics and Law, Zhengzhou, China

第一机构：河南财经政法大学

会议论文集：Proceedings of 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

会议日期：October 20, 2021 - October 22, 2021

会议地点：Changsha, China

语种：英文

外文关键词：Big data - Classification (of information) - Diseases - E-learning - Forecasting - Image classification - Learning algorithms - Machine learning - Medical imaging - Statistical tests

摘要：Breast cancer is the most common cancer for females worldwide. The literature presents a comparison of four machine learning algorithms: Classification Tree, Pruned Classification Tree, Bagging, and Random Forests on the Wisconsin Diagnostic Breast Cancer dataset by measuring the test sensitivity, specificity, precision, negative predictive value, and misclassification error rate. The result shows that the performance of pruned classification tree is better than that of a normal classification tree; aggregated tree models (Bagging and Random Forests) are is better than only use one single decision tree to predict. The best tree model is Random Forests which has the lowest test misclassification error rate of 2.92%. ? 2021 IEEE