Data Mining and decision support systems for clinical application and quality of life

2015 ◽  
Author(s):  
Mario Ferreira ◽  
Luis Paulo Reis ◽  
Brigida Monica Faria ◽  
Joaquim Goncalves ◽  
Alvaro Rocha
Keyword(s):  
Quality Of Life ◽  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Application ◽  
Support Systems

scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

The Information Quality of Databases

2005 ◽  
pp. 281-285 ◽  
Author(s):  
InduShobha Chengalur-Smith ◽  
M. Pamela Neely ◽  
Thomas Tribunella
Keyword(s):  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Information Quality ◽  
Support Systems ◽  
Transaction Processing ◽  
Quality Of Information ◽  
Transaction Processing Systems

A database is only as good as the data in it. Transaction-processing systems and decision-support systems provide data for strategic planning and operations. Thus, it is important to not only recognize the quality of information in databases, but also to deal with it. Research in information quality has addressed the issues of defining, measuring and improving quality in databases; commercial tools have been created to automate the process of cleaning and correcting data; and new laws have been created that deal with the fallout of poor information quality. These issues will be discussed in the next sections.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN DALAM MENENTUKAN JAMUR TIRAM PUTIH TERBAIK MENGGUNAKAN LOGIKA FUZZY

2019 ◽  
Vol 3 (1) ◽  
pp. 73
Author(s):  
Yustria Handika Siregar ◽  
Adi Widarma
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Oyster Mushroom ◽  
Oyster Mushrooms

Abstract - White oyster mushroom is a micro business that is popular in most parts of Indonesia. At present many farmers cultivate white oyster mushrooms. In order for the quality of mushrooms to be optimal, the farmers in the cultivation of white oyster mushrooms always attend training. The quality of oyster mushrooms can be considered by factors that affect the growth of oyster mushrooms themselves. The good quality of mushrooms can increase the selling value of the mushrooms themselves. The decision support system can be used to determine the quality of oyster mushrooms. In this study also the implications of fuzzy logic. It is hoped that the farmers can use the quality of the best oyster mushrooms.Keywords - Decision Support Systems, Best Quality, White Oyster Mushrooms and Fuzzy Logic.  Abstract - Jamur  tiram  putih  merupakan  usaha  mikro  yang  populer  sebagian  besar  di  wilayah  Indonesia. Pada saat ini banyak para petani yang membudidayakan jamur tiram putih. Agar kualitas jamur dapat optimal maka para petani dalam budidaya jamur tiram putih selalu mengikuti pelatihan- pelatihan.  Kualitas  jamur  tiram  dapat  diperhatikan  dengan  faktor-faktor  yang  mempengaruhi pertumbuhan  jamur  tiram  itu  sendiri.  Kualitas  jamur  yang  baik  dapat  meningkatkan  nilai  jual jamur  itu  sendiri.  Sistem  pedukung  keputusan  dapat  digunakan  dalam  menentukan  kualitas jamur  tiram.  Dalam  penelitian  ini  juga  di  impletasikan  logika  fuzzy.  Diharapkan  dapat digunakan para petani dalam menentukan kualitas jamur tiram terbaik. Kata kunci - Sistem Pendukung Keputusan, Kualitas Terbaik, Jamur Tiram Putih dan Logika Fuzzy.

scholarly journals Seleksi Penyerang Utama Menggunakan K-Means Clustering Dan Sistem Pendukung Keputusan Metode Topsis

2017 ◽  
Vol 2 (1) ◽  
pp. 37-48
Author(s):  
Renenata Ardilesmana Siregar
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Ideal Solution ◽  
Major Player ◽  
The Right ◽  
Selection Of ◽  
The Ideal

Untuk  menentukan penyerang ideal dalam sepak bola agar sesuai karakter dan kriteria yang diharapkan,  diperlukan  pelatih  yang  mempunyai naluri  tajam  dan  juga  sistem  yang  bisa membantu pelatih dalam memberikan pilihan. Biasanya dalam proses penentuan pemain masih dilakukan  secara  manual dengan melihat dari karakter dan kriteria dari pemain tersebut. Tetapi terkadang hanya dengan melihat dari karakter dan kriteria dari pemain tersebut saja masih kurang cukup sehingga jauh dari apa yang diharapkan. Untuk  mempermudah dalam pemilihan penyerang ideal, maka diperlukan suatu sistem yang dapat membantu pelatih untuk  memilih penyerang yang dibutuhkan sesuai dengan kebutuhan tim yaitu dengan menggunakan teknik K-Means Clustering dalam metode data mining sebagai proses dalam menyeleksi pemain untuk bergabung  dalam  suatu  tim  dan  juga  didukung  dengan  metode  Sistem  Pendukung Keputusan (Decision Support Systems) The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) sebagai proses dalam menentukan penyerang yang akan bermain sebagai pemain utama dalam tim yang menggunakan beberapa kriteria untuk  memilih pemain yang tepat. Dengan hasil penelitian ini, diharapkan dapat membantu pelatih dalam proses seleksi pemain dan dapat mengubah cara penilaian terhadap sifat subjektif agar lebih obyektif dalam pengambilan keputusan. Kata Kunci :Data Mining, K-Means Clustering, Sistem Pendukung Keputusan To  determine  the  ideal  attacker  in football to match the expected character and criteria, a  coach who has a sharp instinct and a system that can assist the coach in providing choices. Usually  in  the  process  of  determining  the  player  is  still  done  manually  by  look ing  at  the characters  and  criteria  of  the  player.  But  sometimes just by look ing at the characters and criteria of the player is still not enough so far from what is expected. To facilitate the selection of ideal attackers, a system that can help the trainer to select the attacker needed according to the needs of the team is by using K-Means Clustering technique in the method of data mining as a process in selecting players to join a team and also supported by Decision Support Systems method The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is the process of determining which attack er will play as a major player in the team using multiple criteria to select the right player. With the results of this study, it is expected to assist trainers in the selection process of players and can change the way the assessment of the subjective nature to be more objective in decision making. Keywords: Data Mining, K-Means Clustering, Decision Support System.  

scholarly journals Decision and decision makers

2010 ◽  
Author(s):  
Anuta Porutiu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Technologies ◽  
Support Systems ◽  
Policy Makers ◽  
Systemic Analysis ◽  
New Information ◽  
Enterprise Success

In the current economic context, decision making requires complex and multiple actions on the part of the policy makers, who are more challenged than in previous situations, due to the crisis that we are facing. Decision problems cannot be solved by focusing on manager’s own experience or intuition, but require constant adaptation of the methods used effectively in the past to new challenges. Thus, a systemic analysis and modeling of arising issues is required, resulting in the stringent use of Decision Support Systems (DSS), as a necessity in a competitive environment. DSS optimize the situation by getting a timely decision because the decision making process must acquire, process and interpret an even larger amount of data in the shortest possible time. A solution for this purpose is the artificial intelligence systems, in this case Decision Support Systems (DSS), used in a wider area due to expansion of all the new information technologies in decisionmaking processes. These substantial cyber innovations have led to a radical shift in the relationship between enterprise success and quality of decisions made by managers.

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