Monitoring and Evaluation of Human Nutrition and Health Based on Big Data Analysis

Abstract

The balanced consumption of nutrients is essential to human health. This research examines human nutrition and health monitoring and evaluation using extensive data analysis (HHH). Few attempts have been made thus far to harvest and evaluate HHH monitoring data. To ensure the efficacy of these data, extensive data analysis was conducted to analyze and monitor HHH. The authors created a Bayesian network (BN) to monitor HHH after describing the HHH optimization method. The suggested network serves four primary purposes: extracting features from dietary nutrients, classifying foods according to their best nutritional value, diagnosing undernutrition, and offering replacement suggestions. In conclusion, a strategy for massive data processing and selecting the primary controlling element was provided for nutrition monitoring. Experiments demonstrated the relative test error of the proposed network and confirmed its efficacy.