基于CNN模型的在线血库管理数据挖掘技术设计

I. Jacob, P. Darney
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引用次数: 0

摘要

血库是负责储存血液以供输血给需要的病人的组织。血库的首要目标是可靠并确保患者获得相关的无毒血液,以避免与输血相关的并发症,因为血液是一种重要的医疗资源。如果血库管理包括许多人工过程,血库很难在血液储存和输血过程中提供高水平的精度、可靠性和自动化。本研究框架提出使用CNN模型分类方法维护血库记录。在CNN方法的预处理中,对数据集进行标记并设置供体的资格。这将使定期献血者更容易定期向慈善人士和组织献血。一些机器学习技术提供了自动网站更新。Jupyter笔记本被用来分析献血者的数据集,使用决策树、神经网络和冯·贝斯技术。所提出的方法通过网站在线操作。此外,还保留了献血者的性别、体重指数、血压水平和献血频率等资格状况。最后,将不同的机器学习算法与建议的框架进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
A blood bank is the organisation responsible for storing blood to transfuse it to the patients in need. The primary goal of a blood bank is to be reliable and ensure that patients get the relevant non-toxic blood to avoid transfusion-related complications since blood is a critical medicinal resource. It is difficult for the blood banks to offer high levels of precision, dependability, and automation in the blood storage and transfusion process if blood bank administration includes many human processes. This research framework is proposing to maintain blood bank records using CNN model classification method. In the pre-processing of CNN method, the datasets are tokenized and set the donor’s eligibility. It will make it easier for regular blood donors to donate regularly to charitable people and organizations. A few machine learning techniques offer the automated website updation. Jupyter note book has been used to analyze the dataset of blood donors using decision trees, neural networks, and von Bays techniques. The proposed method operates online through a website. Moreover, the donor's eligibility status with gender, body mass index, blood pressure level, and frequency of blood donations is also maintained. Finally, the comparison of different machine learning algorithms with the suggested framework is tabulated.
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