Analysis of Food Nutrients Impact for Improving Public Health Using Multi Features Deep Recurrent Neural Network for Health Care Management

The food nutrients in daily life is incredible source to make good food for good life cycle. In recent analysis, the new food styles and ingredients affects the public health especially children to cause various disease and impacts which leads dangerous to healthier life. By analyzing food nutrients and presence healthy ingredients in daily life is important. World health organization pays number of research to healthcare management and suggestions to improve the public health by recommending different protocols. The risk is identifying presence of nutrients scaling is tedious due to prevailing techniques machine learning techniques does not provide good results to make better good food recommendation. The problem is more data labels and features are need to analyze which increase the false rate to reduce accuracy. To address the issue, to propose a deep LSTM gated recurrent neural network (LSTM-GRNN) based on Support vector feature selection (SVFS) to identify the presence of food nutrients to recommend the good food to improve the public health. Initially the food product and nutrients scaling logs are collected and to make preprocessing based on C-score normalization form feature labels. Then nutrients scaling impact rate is analyzed for each food ingredients presence by extracting the margins from features list. The important feature is selected by using SVSR to reduce the feature margins based on support vector. Then the selected features are trained into LSTM unit with recurrent neural network to identify the presence of nutrients rate in each food and to categorize the class by presence. The higher scaling rate of class is considered as good food to recommend for healthy facts. The proposed system attains higher detection accuracy in precision, recall rate and f1 measure, also the lower false negative rate increase the performance of accuracy compared to the existing system.