Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Author:

Aljawarneh ShadiORCID,Anguera Aurea,Atwood John William,Lara Juan A.,Lizcano David

Abstract

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Signal Processing

Reference95 articles.

1. F. Shadabi, D. Sharma, Artificial intelligence and data mining techniques in medicine – success stories. Int Conf BioMedical Eng Inform 1, 235 (2008)

2. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From data mining to knowledge discovery: an overview, advances in knowledge discovery and data mining. eds. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. AAAI Press/The MIT Press. 1-34 (1996).

3. J. Wu, L. Zhang, S. Yin, H. Wang, G. Wang, J. Yuan, Differential diagnosis model of hypocellular myelodysplastic syndrome and aplastic anemia based on the medical big data platform. Complexity 2018 (2018). https://doi.org/10.1155/2018/4824350

4. S. Mukherjee, Malignant mesothelioma disease diagnosis using data mining techniques. Appl Artif Intell 32(3), 293–308 (2018). https://doi.org/10.1080/08839514.2018.1451216

5. B. G. Ma Bai, B. M. Nalini, J. Majumdar, Analysis and detection of diabetes using data mining techniques—a big data application in health care. In: Shetty N., Patnaik L., Nagaraj H., Hamsavath P., Nalini N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing. 882 (2019)

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Comprehensive Evaluation of Standard Data Warehousing and Data Mining Techniques in the Field of Business;2024 6th International Conference on Management Science and Industrial Engineering;2024-04-24

2. Feature Selection-based Machine Learning Comparative Analysis for Predicting Breast Cancer;Applied Artificial Intelligence;2024-04-10

3. A method for measuring similarity of time series based on series decomposition and dynamic time warping;Applied Intelligence;2022-07-09

4. Applications of Sensor Networks and Remote Sensing in Environmental Sustainability: A Review;2022 International Conference on Engineering & MIS (ICEMIS);2022-07-04

5. mHealth: A Secure Health Monitoring System for Diabetes;2022 International Conference on Engineering & MIS (ICEMIS);2022-07-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3