Health Analysis and Recommendation Based on Food

Abstract

Suitable nutritional diets have been widely recognized as important measures to prevent and control non-communicable diseases (NCDs). However, there is little research on nutritional ingredients in food now, which are beneficial to the rehabilitation of NCDs. In this project, we profoundly analyzed the relationship between nutritional ingredients and diseases by using data mining methods. First, more than n number of diseases was obtained, and we collected the recommended food and taboo food for each disease. The experiments on real life data show that our method based on data mining improves the performance compared with the traditional statistical approach. We can assist doctors and disease researchers to find out positive nutritional ingredients that are conducive to the rehabilitation of the diseases as accurately as possible. At present, some data is not available, because they are still in the medical verification. The dataset uploaded will be preprocessed, Feature Extraction, noisy data will be removed, and classification of dataset will take places using random forest algorithm based on this analysis the diseases prediction takes places for the food intake by the individual.