Data Mining Techniques for the Classification of Medical Cases: A Survey

Oluwaseun Priscilla Olawale, Fezile Ozdamli, Kamil Dimililer
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引用次数: 1

Abstract

One of the rich data fields is the biomedical realm. There are detailed biomedical records, ranging from clinical conditions to different types of biochemical data and imaging equipment outputs. However, the manual extraction and transformation of biomedical patterns into mechanically comprehensible information is a cumbersome challenge since the biomedical field includes broad, dynamic, and complex knowledge. The main focus of this study is to analyze some of the available data mining patterns for the classification of medical cases with the systematic literature review method. Its emphasis is on studying techniques that are commonly used for the prognosis, classification, prediction, and treatment-related to recurrent and significant diseases like cancer, hepatitis, and cardiac diseases. Data mining can enhance healthcare choices and patient survival time. The researchers of this study hope that this research provides information on the data mining classification algorithms used to study medical cases that are not even mentioned in this study.
医学案例分类的数据挖掘技术综述
其中一个丰富的数据领域是生物医学领域。有详细的生物医学记录,从临床情况到不同类型的生化数据和成像设备输出。然而,由于生物医学领域包含广泛、动态和复杂的知识,人工提取生物医学模式并将其转换为机械可理解的信息是一项繁琐的挑战。本研究的主要重点是运用系统文献综述的方法,分析一些可用的医学病例分类数据挖掘模式。它的重点是研究通常用于预后、分类、预测和治疗与复发和重大疾病相关的技术,如癌症、肝炎和心脏病。数据挖掘可以提高医疗保健选择和患者生存时间。本研究的研究人员希望本研究能够提供本研究中甚至没有提到的用于研究医学案例的数据挖掘分类算法的信息。
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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