DiabPrednet: development of attention-based long short-term memory-based diabetes prediction model with optimal weighted feature fusion mechanism

Machine learning is a computer technique that automatically learns from experience and enhances the effectiveness of producing more precise diabetes predictions. However, large, inclusive, high-quality datasets are needed for training the machine learning networks. In this research work, attention-based approaches are designed for predicting diabetes in the affected individuals. Initially, the collected diabetes data is given into the data cleaning to get noise-free data for the prediction task. Here, extracted feature set 1 is extracted from the Auto encoder, and extracted feature set 2 is extracted from the 1-Dimensional Convolutional Neural Network (1D-CNN). These two sets of extracted features are fused in the adaptive way that is weighted feature fusion. Here, the weight of the selected features is optimized by an Enhanced Path Finder Algorithm (EPFA) to get more accurate results. The weighted fused features are employed for the diabetes prediction phase, in which the developed Attention-based Long Short Term Memory (ALSTM) with architecture optimization by improved PFA for predicting diabetes in affected one. Throughout the result analysis, the designed method attains 95% accuracy and 92%precision rate. Finally, the analysis is made by the proposed and existing prediction methods to showcase the effective performance.