Personalized optimal nutrition lifestyle for self obesity management using metaalgorithms

Abstract

Precision medicine applies machine learning methods to estimate the personalized optimal treatment decision based on individual information, such as genetic data and medical history. The main purpose of self obesity management is to develop a personalized optimal life plan that is easy to implement and adhere to, thereby reducing the incidence of obesity and obesity-related diseases. The methodology comprises three components. First, we apply catboost, random forest and lasso covariance test to evaluate the importance of individual features in forecasting body mass index. Second, we apply metaalgorithms to estimate the personalized optimal decision on alcohol, vegetable, high caloric food and daily water intake respectively for each individual. Third, we propose new metaalgorithms named SX and SXwint learners to compute the personalized optimal decision and compare their performances with other prevailing metalearners. We find that people who receive individualized optimal treatment options not only have lower obesity levels than others, but also have lower obesity levels than those who receive ’one-for-all’ treatment options. In conclusion, all metaalgorithms are effective at estimating the personalized optimal decision, where SXwint learner shows the best performance on daily water intake.

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

“…This represents a step forward in ML functional complexity, from the identification of contributing factors to the capability of producing interventional solutions. Many recent studies have developed models for providing “personally optimized recommendations.” [ 41 ] Taking into account personal information, these systems produce recommendations that differed from the ``general rule’’ and were found to have better clinical outcomes in reducing body mass index (BMI). [ 41 ] As discussed previously, ML has been extensively used among various cohorts and subpopulations to identify major nutritional factors contributing to disease.…”

“…Many recent studies have developed models for providing “personally optimized recommendations.” [ 41 ] Taking into account personal information, these systems produce recommendations that differed from the ``general rule’’ and were found to have better clinical outcomes in reducing body mass index (BMI). [ 41 ] As discussed previously, ML has been extensively used among various cohorts and subpopulations to identify major nutritional factors contributing to disease. [ 42 ] This highlights the significance of nutritional factors in predicting and managing health.…”