scholarly journals THE IMPORTANCE OF NORMALIZATION METHODS FOR MINING MEDICAL DATA

2015 ◽  
Vol 14 (8) ◽  
pp. 6014-6020
Author(s):  
Gheorghe Mihaela ◽  
Petre Stefania Ruxandra
Keyword(s):  
Data Mining ◽  
Health Informatics ◽  
Medical Information ◽  
Data Interpretation ◽  
Medical Data ◽  
Data Sets ◽  
Research Papers ◽  
Data Mining Techniques ◽  
Normalization Methods ◽  
The Impact

Over the past decades, the field of medical informatics has been growing rapidly and has drawn the attention of many researchers. The digitization of different medical information, including medical history records, research papers, medical images, laboratory analysis and reports, has generated large amounts of data that need to be handled. As the rate of data acquisition is greater than the rate of data interpretation, new computational technologies are needed in order to manage the resulted repositories of medical data and to extract relevant knowledge from them. Such methods are provided by data mining techniques, which are used for discovering meaningful patterns and trends within the data and help improving various aspects of health informatics. In order to apply data mining techniques, the data needs to be cleansed and transformed, normalization being one of the most important pre-processing methods that accomplish this purpose.This paper aims to present the impact of applying different data normalization methods, on the performance obtained with the K-Nearest Neighbour algorithm on medical data sets.

A Review of Data Mining Techniques and Applications

2017 ◽  
Vol 21 (1) ◽  
pp. 31-48 ◽  
Author(s):  
Ratchakoon Pruengkarn ◽  
◽  
Kok Wai Wong ◽  
Chun Che Fung
Keyword(s):  
Data Mining ◽  
Health Informatics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Huge Number ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
The Past ◽  
Number Of Publications

Data mining is the analytics and knowledge discovery process of analyzing large volumes of data from various sources and transforming the data into useful information. Various disciplines have contributed to its development and is becoming increasingly important in the scientific and industrial world. This article presents a review of data mining techniques and applications from 1996 to 2016. Techniques are divided into two main categories: predictive methods and descriptive methods. Due to the huge number of publications available on this topic, only a selected number are used in this review to highlight the developments of the past 20 years. Applications are included to provide some insights into how each data mining technique has evolved over the last two decades. Recent research trends focus more on large data sets and big data. Recently there have also been more applications in area of health informatics with the advent of newer algorithms.

scholarly journals Study and Analysis of Medical Data Mining Techniques in Healthcare for Heart Disease Using Hybrid Approach

2020 ◽  
Author(s):  
Khodke harish Eknath ◽  
Yadav S K ◽  
Kyatanavar D N
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Research Work ◽  
Hybrid Approach ◽  
Medical Data ◽  
Data Sets ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Inductive Type ◽  
The Right

Information mining frameworks are exhaustively used in coronary affliction for affirmation and figure. As heart condition is that the essential clarification for death for individuals, recognizing confirmation . The work proposed is inductive type and needs deep analysis of the data to ensure the right predictions on the data sets provided. A sample dataset of patients for heart disease will be collected from repository. It involves the steps and procedure. The proposed research work can be carried out step by step to conclude it with the accurate results.

Bibliomining for Library Decision-Making

2011 ◽  
pp. 341-345
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Large Data ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The People ◽  
The World ◽  
Use Of The Internet

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-governmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution. Use of data mining to examine library data might be aptly termed bibliomining. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

An Assessment of the Impact of Rheological Properties on Rate of Penetration Using Data Mining Techniques

2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  
Keyword(s):  
Data Mining ◽  
Rheological Properties ◽  
Rate Of Penetration ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

An Assessment of the Impact of Rheological Properties on Rate of Penetration Using Data Mining Techniques

2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  
Keyword(s):  
Data Mining ◽  
Rheological Properties ◽  
Rate Of Penetration ◽  
Data Mining Techniques ◽  
Using Data ◽  
The Impact

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

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