scholarly journals Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis

2013 ◽  
Vol 06 (03) ◽  
pp. 85-97 ◽  
Author(s):  
Rashedur M. Rahman ◽  
Farhana Afroz
Keyword(s):  
Data Mining ◽  
Diabetes Diagnosis ◽  
Classification Techniques ◽  
Mining Tools

Data Mining in Diabetes Diagnosis and Detection

2008 ◽  
pp. 1817-1824 ◽  
Author(s):  
Indranil Bose
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Information Age ◽  
Diabetic Patients ◽  
Diabetes Diagnosis ◽  
Inference Rules ◽  
The World ◽  
The Usa ◽  
Detection And Diagnosis ◽  
Mining Tools

Diabetes is a disease worrying hundreds of millions of people around the world. In the USA, the population of diabetic patients is about 15.7 million (Breault et al., 2002). It is reported that the direct and indirect cost of diabetes in the USA is $132 billion (Diabetes Facts, 2004). Since there is no method that is able to eradicate diabetes, doctors are striving for ways to fight this doom. Researchers are trying to link the cause of diabetes with patients’ lifestyles, inheritance information, age, and so forth in order to get to the root of the problem. Due to the prevalence of a large number of responsible factors and the availability of historical data, data mining tools have been used to generate inference rules on the cause and effect of diabetes as well as to help in knowledge discovery in this area. The goal of this chapter is to explain the different steps involved in mining diabetes data and to show, using case studies, how data mining has been carried out for detection and diagnosis of diabetes in Hong Kong, USA, Poland, and Singapore.

Data Mining in Diabetes Diagnosis and Detection

2011 ◽  
pp. 257-261
Author(s):  
Indranil Bose
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Information Age ◽  
Diabetic Patients ◽  
Diabetes Diagnosis ◽  
Inference Rules ◽  
The World ◽  
The Usa ◽  
Detection And Diagnosis ◽  
Mining Tools

Diabetes is a disease worrying hundreds of millions of people around the world. In the USA, the population of diabetic patients is about 15.7 million (Breault et al., 2002). It is reported that the direct and indirect cost of diabetes in the USA is $132 billion (Diabetes Facts, 2004). Since there is no method that is able to eradicate diabetes, doctors are striving for ways to fight this doom. Researchers are trying to link the cause of diabetes with patients’ lifestyles, inheritance information, age, and so forth in order to get to the root of the problem. Due to the prevalence of a large number of responsible factors and the availability of historical data, data mining tools have been used to generate inference rules on the cause and effect of diabetes as well as to help in knowledge discovery in this area. The goal of this chapter is to explain the different steps involved in mining diabetes data and to show, using case studies, how data mining has been carried out for detection and diagnosis of diabetes in Hong Kong, USA, Poland, and Singapore.

Classification Techniques and Data Mining Tools Used in Medical Bioinformatics

2019 ◽  
pp. 105-126
Author(s):  
Satish Kumar David ◽  
Amr T. M. Saeb ◽  
Mohamed Rafiullah ◽  
Khalid Rubeaan
Keyword(s):  
Data Mining ◽  
Genomic Analysis ◽  
Resistance Pattern ◽  
Medical Applications ◽  
Classification Analysis ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Drug Resistance Pattern ◽  
Mining Tools

Increasing volumes of data with the increased availability information mandates the use of data mining techniques in order to gather useful information from the datasets. In this chapter, data mining techniques are described with a special emphasis on classification techniques as one important supervised learning technique. Bioinformatics tools in the field for medical applications especially in medical microbiology are discussed. This chapter presents WEKA software as a tool of choice to perform classification analysis for different kinds of available data. Uses of WEKA data mining tools for biological applications such as genomic analysis and for medical applications such as diabetes are discussed. Data mining offers novel tools for medical applications for infectious diseases; it can help in identifying the pathogen and analyzing the drug resistance pattern. For non-communicable diseases such as diabetes, it provides excellent data analysis options for analyzing large volumes of data from many clinical studies.

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

DATA MINING FOR THE MANAGEMENT OF SOFTWARE DEVELOPMENT PROCESS

2004 ◽  
Vol 14 (06) ◽  
pp. 665-695 ◽  
Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS
Keyword(s):  
Data Mining ◽  
Software Development ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Development Process ◽  
A Priori ◽  
Post Mortem ◽  
Software Development Process ◽  
A Priori Analysis ◽  
Mining Tools

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.

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