Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

2008 ◽  
pp. 391-399
Author(s):  
Markos G. Tsipouras ◽  
Themis P. Exarchos ◽  
Dimitrios I. Fotiadis ◽  
Aris Bechlioulis ◽  
Katerina K. Naka
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Analysis ◽  
Cardiovascular Diseases ◽  
Medical Information ◽  
Medical Decision ◽  
Fuzzy Modelling ◽  
Analysis Techniques ◽  
Clinical Environments ◽  
Artery Disease

This article addresses the decision support regarding cardiovascular diseases, using computer-based methods, focusing on the coronary artery disease (CAD) diagnosis and on the prediction of clinical restenosis in patients undergoing angioplasty. Methods reported in the literature are reviewed with respect to (i) the medical information that are employing in order to reach the diagnosis and (ii) the data analysis techniques used for the creation of the CDSSs. In what concerns medical information, easily and noninvasively-obtained data present several advantages compared to other types of data, while data analysis techniques that are characterized by transparency regarding their decisions are more suitable for medical decision making. A recently developed approach that complies with the above requirements is presented. The approach is based on data mining and fuzzy modelling. Using this approach, one CDSS has been developed for each of the two cardiovascular problems mentioned above. These CDSSs are extensively evaluated and comments about the discovered knowledge are provided by medical experts. The later is of great importance in designing and evaluating CDSSs, since it allows them to be integrated into real clinical environments.

Integrating OLAP/SOLAP in E-Business Domains

2011 ◽  
Vol 24 (3) ◽  
pp. 45-60
Author(s):  
Ben Ali ◽  
Samar Mouakket
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
United Arab Emirates ◽  
Decision Makers ◽  
Job Seekers ◽  
Job Market ◽  
Analysis Techniques ◽  
Analytical Processing ◽  
User Friendly

E-business domains have been considered killer domains for different data analysis techniques. Most researchers have examined data mining (DM) techniques to analyze the databases behind E-business websites. DM has shown interesting results, but this technique presents some restrictions concerning the content of the database and the level of expertise of the users interpreting the results. In this paper, the authors show that successful and more sophisticated results can be obtained using other analysis techniques, such as Online Analytical Processing (OLAP) and Spatial OLAP (SOLAP). Thus, the authors propose a framework that fuses or integrates OLAP with SOLAP techniques in an E-business domain to perform easier and more user-friendly data analysis (non-spatial and spatial) and improve decision making. In addition, the authors apply the framework to an E-business website related to online job seekers in the United Arab Emirates (UAE). The results can be used effectively by decision makers to make crucial decisions in the job market of the UAE.

scholarly journals Medical information system for the rapid screening of cardiovascular risk in patients after coronary stenting

2020 ◽  
Vol 35 (4) ◽  
pp. 103-110
Author(s):  
G. S. Pushkarev ◽  
V. A. Kuznetsov ◽  
O. A. Guskova ◽  
L. M. Malishevsky
Keyword(s):  
Information System ◽  
Decision Support ◽  
Mortality Risk ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Coronary Stenting ◽  
Medical Decision ◽  
Medical Information System ◽  
Medical Decision Support ◽  
Risk Scale

Aim. To develop and implement a decision support system for a software product – medical information system “1C: Medicine” in the form of calculator for assessment of the absolute risk of death from cardiovascular diseases (CVD) and to show the prospects of using this system for patients with coronary artery disease (CAD) after coronary stenting.Material and Methods. The medical information system “1C: Medicine” software interface was developed in Tyumen Cardiology Research Center. It was designed to assess 10-year absolute total mortality risk from CVD in males of working age (Tyumen Risk Scale (TRS)) to provide medical decision support. The program was tested in 764 male patients from the Prospective Registry of Percutaneous Coronary Interventions (PCI). The mean age of patients was 56.9 ± 8.8 years. All death cases, recorded within a year after PCI (n = 23), were used as the status check variable. The following algorithms were chosen to compare the predictive accuracy of the integrated model: PROCAM and FRAMINGHAM. The Schwarz information test and ROC analysis data were used to assess the predictive accuracy of the models.Results. The values of Schwarz’s criterion in males were 283 for TRS, 235 for PROCAM, and 490 for FRAMINGHAM model. AUC indicator for TRS was 0.655 (95% CI 0.510–0.800), suggesting the satisfactory quality of resulting model. AUC indicators for FRAMINGHAM and PROCAM algorithms were 0.599 (95% CI 0.442–0.757) and 0.653 (95% CI 0.509–0.796), respectively.Conclusion. The created TRS, integrated into the medical information system with psychosocial factors, may be quickly and successfully implemented to determine mortality risk in CAD patients within one year after coronary stenting. The TRS has an advantage over the traditional FRAMINGHAM risk scale and non-inferior to the PROCAM scale. Therefore, TRS may be used as a medical decision support program.

scholarly journals Applied aspects of data mining for decision support at the regional health system

2021 ◽  
Vol 2094 (3) ◽  
pp. 032006
Author(s):  
G G Rapakov ◽  
A A Sukonshchikov ◽  
A N Shvetsov ◽  
V A Gorbunov ◽  
O Ja Kravets
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Cardiovascular Risk ◽  
Arterial Hypertension ◽  
Clustering Algorithm ◽  
Information Management System ◽  
Medical Decision ◽  
Regional Health ◽  
Medical Decision Support ◽  
Medical Decision Support Systems

Abstract The article presents the results of research of Data Mining methods with Microsoft SQL Server. Microsoft Clustering algorithm was used for improving the effectiveness of medical prevention and treatment in a cohort of patients with arterial hypertension. There are rationales for monitoring of cardiovascular risk and desire to correct the risk with Data Mining at medical decision support systems. Authors used medical and sociological monitoring data from regional clinical hospital. The segmentation of arterial hypertension patients was performed using Microsoft Clustering algorithm. As a result, a quantitative assessment of the population profile for patients with arterial hypertension was obtained. The authors presented diagrams and profiles of clusters. They were compared. The developed approach is applied for decision support at regional health information management system for reduce of cardiovascular risk.

scholarly journals A Novel Automatic Way of Finding Direct and Linear Sequential Inherent Relationships in the Data

2020 ◽  
Vol 9 (4) ◽  
pp. 1896-1903
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Mining Machine ◽  
Analysis Techniques ◽  
Proposed Model ◽  
Simulation Based ◽  
Machine Leaning

Finding linear sequential relationships (LSRs) in the data and applying them for obtaining fruitful results is an essential task in many modern day to day useful real and simulation based applications. In many previous research applications many research people usually assumed that there exists certain relationships in the data and then they have tried to bring forth some useful results after processing the selected datasets using one more data mining, machine leaning, and big data techniques. Take it for granted assumptions on the data may not be true in all the cases and in all the applications. The purpose of the present study is to bring out some automatic, smart, simple, scalable, fruitful and useful data analysis techniques after analyzing the datasets in the hand and at the same time assumptions on the data are not considered just like the general fashion of take it for granted option. The proposed model is particularly useful and applicable for finding the drug to disease relationships.

scholarly journals A Survey on Data Mining Algorithms and Techniques in Medicine

2017 ◽  
Vol 1 (3) ◽  
pp. 61 ◽  
Author(s):  
Kasra Madadipouya
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Performance Metrics ◽  
Support Systems ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Data Mining Algorithms ◽  
Medical Decision Support Systems ◽  
Mining Algorithms

Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the population, symptoms, pathology of a disease, family history, and test results, would be useful to physicians and hospitals. This paper provides an in-depth review of available data mining algorithms and techniques. In addition to that, data mining applications in medicine are discussed as well as techniques for evaluating them and available applications of performance metrics.

Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling

2011 ◽  
pp. 1520-1530 ◽  
Author(s):  
Markos G. Tsipouras ◽  
Themis P. Exarchos ◽  
Dimitrios I. Fotiadis ◽  
Aris Bechlioulis
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Predictive Modelling ◽  
Clinical Decision ◽  
Medical Problems ◽  
Fuzzy Modelling ◽  
Technical Solutions

The widespread availability of new computational methods and tools for data analysis and predictive modelling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, data mining techniques offer methodological and technical solutions to deal with the analysis of medical data and construction of decision support systems. Furthermore, fuzzy modelling deals with the ambiguity inherent in all medical problems. These methods can be used to design and develop clinical decision support systems (CDSSs), which, after evaluated from the experts, can be integrated into clinical environments.

Social Network Analysis

2018 ◽  
pp. 1-18 ◽  
Author(s):  
Somya Jain ◽  
Adwitiya Sinha
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Analysis Techniques ◽  
Text Images ◽  
Application Specific

Over the last decade, technology has thrived to provide better, quicker, and more effective platforms to help individuals connect and disseminate information to other individuals. The increasing popularity of these networks and its huge content in the form of text, images, and videos provides new opportunities for data analytics in the context of social networks. This motivates data mining experts and researchers to deploy various mining apparatus and application-specific tools for analysing the massive, intricate, and dynamic social media knowledge. The research detailed in this chapter would entail major social network concepts with data analysis techniques. Moreover, it gives insight to representation and modelling of social networks with research datasets and tools.

Sign in / Sign up

Export Citation Format

Share Document