Diabetes Mellitus Disease Prediction and Classification using Latent Dirichlet Allocation and Artificial Neural Network Classifier
Keywords:
Artificial Neural Network, Bivariate filter, Diabetes mellitus, Latent Dirichlet Allocation, Pearson correlationAbstract
The Diabetes Mellitus (DM) is known as of the persistent disease which is due to excessive blood sugar levels. When it is left untreated it leads to severe health complications like cardiac disorders, kidney damage and stroke. The existing methods based on machine learning and deep learning approaches faces problem in predicting the diabetes of the patients in a precise manner. Moreover, the classification accuracy was diminished when evaluated for large datasets, so this research introduced an effective classification approach using the combination of Latent Dirichlet Allocation (LDA) and Artificial Neural Network (ANN). The probability distribution function of LDA is combined using the back propagation of ANN where the weights are initialized to perform an effective diabetes classification. The data is obtained from PIMA Dataset and North California State University (NCSU) dataset then the pre-processing is performed using min-max normalization approach. After this, Bivariate filter based feature selection is performed to choose appropriate features that were selected using the bivariate filter method is fed as input to Pearson correlation which selects the effective features based on threshold value. Finally, classification is performed using the proposed ANN-LDA. The results show that suggested method performs better than the existing approaches and achieves classification accuracy of 93%.
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