ENN-Ensemble based Neural Network method for Diabetes Classification
| Autor: | G. L. Aruna Kumari, Jaya Suma G, P. Padmaja |
|---|---|
| Přispěvatelé: | Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Environmental Engineering
Artificial neural network Computer science business.industry General Engineering C4819029320/2020©BEIESP 2249-8958 medicine.disease Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION Diabetes mellitus Diabetes classification diabetes mellitus neural network ensemble learning medicine Artificial intelligence business |
| Popis: | Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENNEnsemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques |
| Databáze: | OpenAIRE |
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