Multi level incremental influence measure based classification of medical data for improved classification

2018 ◽  
Vol 22 (S6) ◽  
pp. 15073-15080
Author(s):  
K. Ananthajothi ◽  
M. Subramaniam
Author(s):  
G.L. Tsirogiannis ◽  
D. Frossyniotis ◽  
J. Stoitsis ◽  
S. Golemati ◽  
A. Stafylopatis ◽  
...  

2019 ◽  
Vol 30 (6) ◽  
pp. 766-792 ◽  
Author(s):  
Wei Wei Cheryl Leo ◽  
Gaurangi Laud ◽  
Cindy Yunhsin Chou

Purpose The purpose of this paper is to develop a concept of service system well-being by presenting its collective conceptualisation and ten key domains. Design/methodology/approach Service system well-being domains were established using multi-level theory and a qualitative case study research design. To validate the domains initially developed from the literature, 19 in-depth interviews were conducted across two case studies that represented the service systems of a hospital and a multi-store retail franchise chain. A multi-stakeholder approach was used to explore the actor’s perspectives about service system well-being. Key domains of service system well-being were identified using deductive categorisation analysis. Findings The findings found evidence of ten key domains of well-being, namely strategic, governance, leadership, resource, community, social, collaborative, cultural, existential and transformational, among service system stakeholders. Research limitations/implications Service system well-being is a collective concept comprising ten domains that emerged at different levels of the service system. The propositions outlined the classification of and interlinkages between the domains. This exploratory study was conducted in a limited service context and focussed on ten key domains. Practical implications Service managers in commercial and social organisations are able to apply the notion of service system well-being to identify gaps and nurture well-being deficiencies within different domains of service-system well-being. Originality/value Based on multi-level theory, the study is the first to conceptualise and explore the concept of service system well-being across multiple actors.


Author(s):  
Sumathi S. ◽  
Indumathi S. ◽  
Rajkumar S.

Text classification in medical domain could result in an easier way of handling large volumes of medical data. They can be segregated depending on the type of diseases, which can be determined by extracting the decisive key texts from the original document. Due to various nuances present in understanding language in general, a requirement of large volumes of text-based data is required for algorithms to learn patterns properly. The problem with existing systems such as MedScape, MedLinePlus, Wrappin, and MedHunt is that they involve human interaction and high time consumption in handling a large volume of data. By employing automation in this proposed field, the large involvement of manpower could be removed which in turn speeds up the process of classification of the medical documents by which the shortage of medical technicians in third world countries are addressed.


2007 ◽  
Vol 12 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Mithun Prasad ◽  
Arcot Sowmya ◽  
Peter Wilson
Keyword(s):  

2005 ◽  
Vol 38 (1) ◽  
pp. 47-62 ◽  
Author(s):  
S. Giordana ◽  
S.J. Sherwin ◽  
J. Peiró ◽  
D.J. Doorly ◽  
Y. Papaharilaou ◽  
...  

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin

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.


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