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Prediction of Pneumonia Using Big Data, Deep Learning and Machine Learning Techniques

机译:使用大数据,深度学习和机器学习技术预测肺炎

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Using big data for prediction analysis along with machine learning or deep learning techniques or algorithms is one the most active areas of research in order to improve the health and the medical science. There is a significant increase in the size of the medical data as well as the complexity in the diagnosis of various diseases. With this being said, the diagnosis or the prediction of many terminal or fatal diseases has seen huge success through deep learning. Among those fatal diseases, pneumonia is one of the greatest threats to the life of a man affecting the lungs leading to lung failure. To diagnose a man with pneumonia, the x-ray of chest is needed, and an expert in the prediction is also required. Hence, it is more convenient to build an automated predictor to predict the pneumonia using the big data deep learning methods. Among all the other techniques, CNN (Convolutional Neural Networks) stand tall and high in this prediction along with other classifiers. Also, pre-training the CNN models for very large datasets that is for big data of healthcare units stands a high chance for accurate classification. A CNN model which is pre-trained along with an efficient feature extraction technique and various classifiers to classify the positive from negative is considered to give highly accurate results. This research work represents the Prediction of Pneumonia using Big Data, Deep Learning and Machine Learning Techniques.
机译:使用大数据进行预测分析以及机器学习或深度学习技术或算法是最活跃的研究领域,以改善健康和医学。医学数据的大小以及各种疾病诊断的复杂性存在显着增加。据说,通过深入学习,许多终端或致命疾病的诊断或预测已经取得了巨大的成功。在那些致命的疾病中,肺炎是对影响肺部失败的人类生命的最大威胁之一。为了诊断患有肺炎的人,需要胸部的X射线,并且还需要预测的专家。因此,建立自动化预测因子更方便使用大数据深度学习方法来预测肺炎。在所有其他技术中,CNN(卷积神经网络)与其他分类器一起在这种预测中高度且高。此外,预先培训了用于非常大的数据集的CNN模型,即用于医疗保健单位的大数据,占准确分类的很大机会。通过高效的特征提取技术和各种分类器预先培训的CNN模型,以分类为阳性的阳性,以提供高精度的结果。本研究工作代表了使用大数据,深度学习和机器学习技术的肺炎的预测。

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