Optimizing Diabetes Prediction: LDA Pre-processing & ANN Classification in Healthcare`
Keywords:
Diabetes Mellitus (DM), Latent Dirichlet Allocation (LDA), Normalization, Pre-processing techniques, Classification accuracy.Abstract
Diabetes mellitus (DM) is a chronic disease that poses significant health risks if not well managed. The current healthcare system is overwhelmed by the impact of DM. Modern machine learning and deep learning methods have a hard time correctly predicting the stages of diabetes and often encounter decreased classification accuracy when dealing with massive datasets. In this work, we provide a novel approach to address these problems by integrating pre-processing with LDA and ANN for classification. By combining the LDA and ANN probability distribution functions by back propagation with initialized weights, our method enhances the accuracy of diabetes categorization. After pre-processing data from the PIMA and NCSU datasets using min-max normalization, bivariate filter-based feature selection is used to identify crucial characteristics. Pearson correlation is used to improve the feature set according to a threshold value, further refining the selected qualities. Our experimental results demonstrate the efficacy of the proposed approach, surpassing even the most cutting-edge methods. By integrating a robust classification model with advanced pre-processing techniques, our strategy produces encouraging outcomes in the accurate prediction of diabetes, which in turn helps to improve healthcare management methods.
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