Hybrid Greylag Goose deep learning with layered sparse network for women nutrition recommendation during menstrual cycle

A complex biological process involves physical changes and hormonal fluctuation in the menstrual cycle. The traditional nutrition recommendation models often offer general guidelines but fail to address the specific requirements of women during various menstrual cycle stages. This paper proposes a novel Optimization Hybrid Deep Learning (OdriHDL) model to provide a personalized health nutrition recommendation for women during their menstrual cycle. It involves pre-processing the data through Missing Value Imputation, Z-score Normalization, and One-hot encoding. Next, feature extraction is accomplished using the Layered Sparse Autoencoder Network. Then, the extracted features are utilized by the Hybrid Attention-based Bidirectional Convolutional Greylag Goose Gated Recurrent Network (HABi-ConGRNet) for nutrient recommendation. The hyper-parameter tuning of HABi-ConGRNet is carried out using Greylag Goose Optimization Algorithm to enhance the model performance. The Python platform is used for the simulation of collected data, and several performance metrics are employed to analyze the performance. The OdriHDL model demonstrates superior performance, achieving a maximum accuracy of 97.52% and enhanced precision rate in contrast to the existing methods, like RNN, CNN-LSTM, and attention GRU. The findings suggest that OdriHDL captures complex patterns between nutritional needs and menstrual symptoms and provides robust solutions to unique physiological changes experienced by women.