A Comparative Study of ML Algorithms for Scenario-agnostic Predictions in Healthcare

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI:10.1109/ISCC55528.2022.9912808

Argyro Mavrogiorgou, S. Kleftakis, N. Zafeiropoulos, Konstantinos Mavrogiorgos, Athanasios Kiourtis, D. Kyriazis

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引用次数: 2

Abstract

The extraction of useful knowledge from collected data has always been the holy grail for enterprises and researchers, supporting efficient decision making, provided service's optimization and profit maximization. However, this task is easier said than done, since it presupposes the application of complex mathematical models/algorithms. Data Analysis has prospered due to the continuous demand to simplify and optimize the knowledge extraction process. Several mechanisms in different domains have been developed, consisting of various techniques to analyze specific data. The need for such mechanisms is even greater in healthcare, since there exist data of different complexity that may provide high-valuable knowledge, if properly analyzed. Considering these challenges, this paper proposes a mechanism for performing Data Analysis in diverse scenarios' healthcare data to extract valuable insights. The mechanism can collect data and apply several Machine Learning algorithms to ensure the best result about the prediction of certain features of the provided data.

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医疗保健中场景不可知预测的ML算法比较研究

从收集的数据中提取有用的知识一直是企业和研究人员的圣杯，支持高效决策，提供服务的优化和利润最大化。然而，这项任务说起来容易做起来难，因为它需要应用复杂的数学模型/算法。由于对知识提取过程的不断简化和优化的需求，数据分析得到了蓬勃发展。在不同的领域已经开发了几种机制，包括各种分析特定数据的技术。在医疗保健领域对这种机制的需求甚至更大，因为存在不同复杂性的数据，如果分析得当，这些数据可能提供高价值的知识。考虑到这些挑战，本文提出了一种在不同场景的医疗保健数据中执行数据分析的机制，以提取有价值的见解。该机制可以收集数据并应用几种机器学习算法，以确保对所提供数据的某些特征进行预测的最佳结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE Symposium on Computers and Communications (ISCC)

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