Determinants and development of a web-based child mortality prediction model in resource-limited settings: A data mining approach

2017 ◽  
Vol 140 ◽  
pp. 45-51 ◽  
Author(s):  
Brook Tesfaye ◽  
Suleman Atique ◽  
Noah Elias ◽  
Legesse Dibaba ◽  
Syed-Abdul Shabbir ◽  
...  
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Child Mortality ◽  
Mortality Prediction ◽  
Resource Limited Settings ◽  
Web Based ◽  
Data Mining Approach ◽  
Resource Limited ◽  
Mortality Prediction Model

Incorporating initial treatments improves the performance of a mortality prediction model based on administrative data

2012 ◽  
Vol 40 (7) ◽  
pp. 2268-2269 ◽  
Author(s):  
Tara Lagu ◽  
Thomas L. Higgins ◽  
Brian H. Nathanson ◽  
Peter K. Lindenauer
Keyword(s):  
Prediction Model ◽  
Administrative Data ◽  
Mortality Prediction ◽  
Model Based ◽  
Mortality Prediction Model

scholarly journals Mortality prediction model for surgery in severe acute pancreatitis

HPB ◽  
2018 ◽  
Vol 20 ◽  
pp. S509
Author(s):  
J. Pereira ◽  
T. Campos ◽  
C. Daniel ◽  
C. Casimiro ◽  
A. Matos ◽  
...  
Keyword(s):  
Acute Pancreatitis ◽  
Prediction Model ◽  
Severe Acute Pancreatitis ◽  
Mortality Prediction ◽  
Mortality Prediction Model

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

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