Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks

Abstract

This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-RDNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal Discriminative Preprocessing Model (NDPM) and bivariant regressive deep artificial neural network with MapReduce (BRDANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-RDNMR conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRDANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers.