用于肺炎和哮喘检测的深度和机器学习

Amani Yahyaoui, N. Yumusak
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引用次数: 2

摘要

机器学习是人工智能的一个分支,广泛应用于医疗领域,用于分析高维医疗数据和早期发现某些危险疾病。肺部疾病继续增加世界上的死亡率。对肺部疾病进行早期、准确的预测,已成为挽救患者生命、方便医生工作的首要需要。本文的重点是分别使用深度学习(DL)和机器学习(ML)技术、深度神经网络(DNN)和k近邻(KNN)方法预测某些胸部疾病,如肺炎和哮喘。这些方法使用来自土耳其迪亚巴克尔医院肺病科的私人数据集进行评估。它由212个样本组成,每个样本有38个输入特征。所获得的结果显示了这些方法检测肺部疾病的有效性,特别是KNN,使用DNN方法的检测准确率为95%和94.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep And Machine Learning Towards Pneumonia And Asthma Detection
Machine Learning is a branch of artificial intelligence widely used in the medical field to analyze high-dimensional medical data and the early detection of certain dangerous diseases. Lung diseases continue to increase the mortality rate in the world. The early and accurate prediction of lung diseases has become a primary necessity to save patient's lives and facilitate doctor's works. This paper focuses on predicting certain chest diseases such as Pneumonia and Asthma using Deep Learning (DL) and Machine Learning (ML) techniques, respectively, the Deep Neural Network (DNN), and the K-nearest Neighbors (KNN) methods. These approaches are evaluated using a private data set from the pulmonary diseases department of Diyarbakir hospital, Turkey. It consists of 212 samples, 38 input characteristics characterize each one. The results obtained showed the effectiveness of these methods to detect pulmonary diseases, particularly the KNN, by giving a detection accuracy of 95% and 94.3% by using the DNN method.
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