采用机器学习算法预测新冠肺炎疫情期间患者(建筑工人)的住院时间。

S Selvakumara Samy, S Karthick, Meghna Ghosal, Sameer Singh, J S Sudarsan, S Nithiyanantham
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引用次数: 1

摘要

在印度这样一个快速发展的国家,建筑业是一个非常无组织的行业。在疫情期间,大量工人受到影响并住院治疗。这种情况使该行业在几个方面付出了沉重代价。这项研究是使用机器学习算法改进建筑公司健康和安全政策的一部分。LOS(住院时间)用于预测患者在医院的住院时间。预测服务水平不仅对医院非常有用,对建筑公司衡量资源和降低成本也非常有用。在大多数医院入院前,预测服务水平已成为重要的一步。在这篇文章中,我们使用了用于重症监护的医疗信息集市(MIMIC III)数据集,并应用了四种不同的机器学习算法:决策树分类器、随机森林、人工神经网络(ANN)和逻辑回归。首先,我进行了数据预处理以清理数据集。在下一步中,我们使用评估函数为chi2的Select Best算法进行函数选择,以执行热编码。然后,我们在训练和测试之间进行了划分,并应用了机器学习算法。用于比较的标准是准确性。在实现算法后,对算法的准确性进行了比较。随机森林的表现最好,达到89%。然后,我们在随机森林上使用网格搜索算法进行超参数调整,以获得更高的精度。最终准确率为90%。这类研究可以通过引入现代计算技术来帮助改善卫生安全政策,也可以帮助优化资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic.

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic.

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic.

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic.

The construction sector in a rapidly developing country like India is a very unorganized sector. A large number of workers were affected and hospitalized during the pandemic. This situation is costing the sector heavily in several respects. This research study was conducted as part of using machine learning algorithms to improve construction company health and safety policies. LOS (length of stay) is used to predict how long a patient will stay in a hospital. Predicting LOS is very useful not only for hospitals, but also for construction companies to measure resources and reduce costs. Predicting LOS has become an important step in most hospitals before admitting patients. In this post, we used the Medical Information Mart for Intensive Care(MIMIC III) dataset and applied four different machine learning algorithms: decision tree classifier, random forest, Artificial Neural Network (ANN), and logistic regression. First, I performed data pre-processing to clean up the dataset. In the next step, we performed function selection using the Select Best algorithm with an evaluation function of chi2 to perform hot coding. We then performed a split between training and testing and applied a machine learning algorithm. The metric used for comparison was accuracy. After implementing the algorithms, the accuracy was compared. Random forest was found to perform best at 89%. Afterwards, we performed hyperparameter tuning using a grid search algorithm on a random forest to obtain higher accuracy. The final accuracy is 90%. This kind of research can help improve health security policies by introducing modern computational techniques, and can also help optimize resources.

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