Improved Feature Selection and Classification for Diabetes Mellitus Using Random Forest-Based U-Net Classifier
Keywords:
Prediction, diabetes, attributes, random forest, U-net.Abstract
In recent times, the prediction of diabetes has become a serious due to the presence of numerous attributes while collecting data. Therefore, many models are developed eradicate such problem to attain increased rate of accuracy during the prediction of diabetes. With varying individuals, the dataset changes dynamically and this leads to fluctuations in the prediction accuracy. The poor-quality outcomes result in poor accuracy and this affects the classification ability of the knowledge mining algorithms. In this paper, the develop a feature selection-based classification modelling using machine learning algorithm that aims at improving the rate of classification accuracy. The study uses random forest classifier as its feature selection tool and then the classification is conducted using deep U-net classifier. The simulation is conducted to test the efficacy of the model against various models over several available datasets. The results of simulation shows that the proposed method achieves higher rate of classification accuracy than the existing methods.
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