| Autor: |
S., Lokesh Nelligere, Vadivel, Sameswari, Kothandapani, Sudha, Mahilraj, Jenifer, Sivaram, P., Ramasamy, Vidhyavathi |
| Předmět: |
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| Zdroj: |
IUP Journal of Computer Sciences; Jan2023, Vol. 17 Issue 1, p46-55, 10p |
| Abstrakt: |
A vast range of Neural Network (NN) applications have been reported in recent research. This paper uses a NN-based classification model for dengue disease prediction. Uncertain NN (UNN) is introduced to classify the dengue disease dataset efficiently. Research can be conducted in a wide range of fields to resolve the issue of missing and uncertain data. Realtime datasets contain a lot of missing attributes or values. Real-time data is used because it is approximately measured and thus pays more attention to disease prediction. The main objective is to develop an efficient feature selection-based classification model to predict dengue disease. The research is carried out for feature model creation and relative analysis to improve dengue virus prediction. The work began by gathering dengue virus datasets from the UCI Machine Learning repository, then, a Genetic Algorithm (GA) is used for feature selection; next, the UNN model is used to create a classification model. Accuracy (A), Specificity (Sp) and Sensitivity (Se) are calculated for justifying the result. A simulation of the proposed method is conducted using the WEKA tool. The proposed classification procedure is found to be the most effective model to predict and detect dengue fever. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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