Zhou Huan, Zhang Pei-Ying, Zou Xiao, Liu Jia, Wang Wen-Jie
School of Business, Hunan University of Technology, Zhuzhou, Hunan, China.
Digit Health. 2023 Aug 31;9:20552076231198643. doi: 10.1177/20552076231198643. eCollection 2023 Jan-Dec.
Chronic diseases have become one of the main causes of premature death all around the world in recent years. The diagnosis of chronic diseases is time-consuming and costly. Therefore, timely diagnosis and prediction of chronic diseases are very necessary.
In this paper, a new method for chronic disease diagnosis is proposed by combining convolutional neural network (CNN) and ensemble learning. This method utilizes random forest (RF) as the base classifier to improve classification performance and diagnostic accuracy, and then combines AdaBoost to successfully replace the Softmax layer of CNN to generate multiple accurate base classifiers while determining their optimal attributes, achieving high-quality classification and prediction of chronic diseases.
To verify the effectiveness of the proposed method, real-world Electronic Medical Records dataset (C-EMRs) was used for experimental analysis. The results show that compared with other traditional machine learning methods such as CNN, K-Nearest Neighbor, and RF, the proposed method can effectively improve the accuracy of diagnosis and reduce the occurrence of missed diagnosis and misdiagnosis.
This study will provide effective information for the diagnosis of chronic diseases, assist doctors in making clinical decisions, develop targeted intervention measures, and reduce the probability of misdiagnosis.
近年来，慢性病已成为全球过早死亡的主要原因之一。慢性病的诊断既耗时又昂贵。因此，慢性病的及时诊断和预测非常必要。
本文提出了一种将卷积神经网络（CNN）与集成学习相结合的慢性病诊断新方法。该方法利用随机森林（RF）作为基础分类器来提高分类性能和诊断准确性，然后结合AdaBoost成功替换CNN的Softmax层，在确定其最优属性的同时生成多个准确的基础分类器，实现慢性病的高质量分类和预测。
为验证所提方法的有效性，使用真实世界电子病历数据集（C-EMRs）进行实验分析。结果表明，与CNN、K近邻和RF等其他传统机器学习方法相比，所提方法能有效提高诊断准确性，减少漏诊和误诊的发生。
本研究将为慢性病诊断提供有效信息，协助医生做出临床决策，制定针对性干预措施，降低误诊概率。
