DocumentCode :
629380
Title :
A novel non-invasive method for prognosis of Dengue fever
Author :
Samundeeswari, P. ; Sandanalakshmi, R.
Author_Institution :
Electron. & Commun. Eng. Dept., Pondicherry Eng. Coll., Puducherry, India
fYear :
2013
fDate :
3-5 April 2013
Firstpage :
682
Lastpage :
686
Abstract :
Prediction of Dengue presents great challenge as the clinical symptoms overlaps with other conventional fever. Dengue viral infection has been reported in more than 100 countries, with total of 2.5 billion people. Testing of prognosis, the stages of dengue such as DF, Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS) under conventional methods needs frequent blood test information. The condition of dengue infected patient becomes more ill from one stage to another and continuous blood test will trouble the patients in terms of wastage of blood and make them restless. In the proposed noninvasive method, the Generalized Regression Neural Network (GRNN) was trained with existing patient´s blood test database in order to perform diagnosis and prognosis of stages. While analyzing the database, for training process it was observed that there is occurrence of missing data in the database that widely affects the training process. The database is classified using adaptive decision tree method to improve the imputation accuracy. In order to fill the missing data, K-Nearest Neighbors (K-NN) imputation method is adapted. The performance of the trained GRNN for early diagnosis of DF and prognosis of stages from the instant blood test information of test patient had been analyzed with actual results and trained Radial Basis Function Neural Network (RBFNN), for adult and child database. It is observed that K-NN provides 95% accuracy; GRNN provides 95% accuracy and 0.875 Area Under the Curve (AUC) for prognosis and diagnosis.
Keywords :
blood; decision trees; diseases; learning (artificial intelligence); medical computing; patient diagnosis; radial basis function networks; regression analysis; Area Under the Curve; DF early diagnosis; DHF; DSS; Dengue Hemorrhagic Fever; Dengue Shock Syndrome; Dengue fever prognosis; Dengue viral infection; GRNN; Generalized Regression Neural Network; K-Nearest Neighbors imputation method; RBFNN; Radial Basis Function Neural Network; adaptive decision tree method; adult database; child database; clinical symptoms; continuous blood test; conventional fever; denque stage diagnosis; denque stage prognosis; instant blood test information; noninvasive method; patient blood test database; training process; Accuracy; Blood; Databases; Decision support systems; Decision trees; Neurons; Training; Dengue; Generalized Regression Neural Networks; K-Nearest Neighbors; Radial Basis Function Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications and Signal Processing (ICCSP), 2013 International Conference on
Conference_Location :
Melmaruvathur
Print_ISBN :
978-1-4673-4865-2
Type :
conf
DOI :
10.1109/iccsp.2013.6577142
Filename :
6577142
Link To Document :
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