Identification of Relevant Medical Parameter Values in Information Systems using Fuzzy Approach

被引:1
|
作者
Wieslaw, Paja [1 ]
机构
[1] Univ Rzeszow, Coll Nat Sci, Pigonia Str 1, PL-35310 Rzeszow, Poland
来源
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021) | 2021年 / 192卷
关键词
feature selection; interval selection; dimensionality reduction; relevance and irrelevance;
D O I
10.1016/j.procs.2021.09.166
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Feature selection is an important process in machine learning. It aims to identify relevant features that describe the problem in the context of the categories distinguished in the problem domain. This paper presents the results of a study on the feasibility of using a discretization and value fuzzification process for numerical attributes to identify significant ranges of attribute values. In medicine it is applicable to identify significant values of parameters, medical indicators so that the size of medical examinations can be reduced and appropriate interpretation of symptoms can be created. The results obtained show that this is possible without losing the quality of classification while reducing the number of attributes. Moreover, by analyzing the obtained value intervals, it is possible to detail the feature selection process and detect irrelevant features not previously identified. (C) 2021 The Authors. Published by Elsevier B.V.
引用
收藏
页码:3915 / 3921
页数:7
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