Paper Info
Title
An Efficient Prediction Model for Diabetic Database Using Soft Computing Techniques
Abstract
Organizations aim at harnessing predictive insights, using the vast real-time data stores that they have accumulated through the years, using data mining techniques. Health sector, has an extremely large source of digital data - patient-health related data-store, which can be effectively used for predictive analytics. This data, may consists of missing, incorrect and sometimes incomplete values sets that can have a detrimental effect on the decisions that are outcomes of data analytics. Using the PIMA Indians Diabetes dataset, we have proposed an efficient imputation method using a hybrid combination of CART and Genetic Algorithm, as a preprocessing step. The classical neural network model is used for prediction, on the preprocessed dataset. The accuracy achieved by the proposed model far exceeds the existing models, mainly because of the soft computing preprocessing adopted. This approach is simple, easy to understand and implement and practical in its approach.
Year
DOI
Venue
2009
10.1007/978-3-642-10646-0_40
RSFDGrC
Keywords
Field
DocType
digital data,efficient prediction model,predictive analytics,vast real-time data store,predictive insight,existing model,data analytics,classical neural network model,preprocessed dataset,pima indians diabetes dataset,soft computing techniques,diabetic database,data mining technique,neural network model,real time data,genetic algorithm,data mining,soft computing,prediction model,relational data
Data mining,Data analysis,Predictive analytics,Computer science,Preprocessor,Artificial intelligence,Imputation (statistics),Soft computing,Artificial neural network,Digital data,Genetic algorithm,Machine learning
Conference
Volume
ISSN
Citations 
5908
0302-9743
1
PageRank 
References 
Authors
0.41
3
5
Name
Order
Citations
PageRank
Veena H. Bhat152.50
Prasanth G. Rao230.77
P. Deepa Shenoy311715.23
K. R. Venugopal426748.80
L M Patnaik51617198.06