训练临床物联网数据和设备互操作性的机器学习模型

2023 12th Mediterranean Conference on Embedded Computing (MECO) Pub Date : 2023-06-06 DOI:10.1109/MECO58584.2023.10154963

Valeryi M. Bezruk, Stanislav A. Krivenko, Oleksandr O. Kyrsanov, Sergii S. Kryvenko, L. Kryvenko

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引用次数: 0

摘要

数据的挖掘、整理、交互分析和可视化以一体化的方式进行。考虑了如何在Python中绘制由XGBoost模型计算的特征重要性。Haar变换改进了数据集的特征工程。接受者工作特征曲线下的面积从基线模型的0.44增加到Haar变换模型的0.82。

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Training the Machine Learning Model for Clinical IoT Data and Device Interoperability

Data exploration, wrangling, and interactive analysis and visualization were made in an integrated way. How to plot feature importance in Python calculated by the XGBoost model was considered. Features engineering in a dataset has been improved with Haar Transform. The area under the receiver operating characteristic curve was increased from 0.44 for the baseline model to 0.82 for Haar Transform Model.

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来源期刊

2023 12th Mediterranean Conference on Embedded Computing (MECO)

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