scholarly journals Knowledge Discovery in Health Care Datasets Using Data Mining Tools

2012 ◽  
Vol 3 (4) ◽  
Author(s):  
MD. ◽  
Dr. Y.K. ◽  
Dr Varun
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Knowledge Discovery ◽  
Using Data ◽  
Mining Tools

The Research on C2C E-Commerce Website Using Data Mining Tools to Improve the Network Marketing Effect

2013 ◽  
Vol 433-435 ◽  
pp. 1885-1889
Author(s):  
Lu Feng ◽  
Zhan Quan Wen ◽  
Jie Mei Lin
Keyword(s):  
Data Mining ◽  
Analysis Method ◽  
Network Marketing ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Using Data ◽  
Object Of Study ◽  
Hyperlink Analysis ◽  
Mining Tools

We used the principle of hyperlink analysis method to mine the website data according to the indicators of the hyperlink analysis. We selected Taobao.com as an object of study. The evaluation indicators of network marketing effect were page views, sales quantity, sales, the number of adding store to bookmark . According to our research, we find Taobao.com stores can use data mining tool to obtain the very good marketing effect.

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 Techniques for an Analysis Of Non-Conventional Methodologies

2010 ◽  
pp. 82-91
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Data Mining ◽  
Alternative Medicine ◽  
Knowledge Discovery ◽  
Western Medicine ◽  
Musculoskeletal Complaints ◽  
Scientific Methods ◽  
Using Data ◽  
Data Elements ◽  
Discovery Algorithms

Some common methodologies in our everyday life are not based on modern scientific knowledge but rather a set of experiences that have established themselves through years of practice. As a good example, there are many forms of alternative medicine, quite popular, however difficult to comprehend by conventional western medicine. The diagnostic and therapeutic methodologies are very different and sometimes unique, compared to that of western medicine. How can we verify and analyze such methodologies through modern scientific methods? We present a case study where data-mining was able to fill this gap and provide us with many tools for investigation. Osteopathy is a popular alternative medicine methodology to treat musculoskeletal complaints in Japan. Using data-mining methodologies, we could overcome some of the analytical problems in an investigation. We studied diagnostic records from a very popular osteopathy clinic in Osaka, Japan that included over 30,000 patient visits over 6 years of practice. The data consists of some careful measurements of tissue electro-conductivity differences at 5 anatomical positions. Data mining and knowledge discovery algorithms were applied to search for meaningful associations within the patient data elements recorded. This study helped us scientifically investigate the diagnostic methodology adopted by the osteopath.

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.

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