OBESEYE: Interpretable Diet Recommender for Obesity Management using
  Machine Learning and Explainable AI
Mrinmoy Roy Srabonti Das Anica Tasnim Protity
Obesity, the leading cause of many non-communicable diseases, occurs mainly
for eating more than our body requirements and lack of proper activity. So,
being healthy requires heathy diet plans, especially for patients with
comorbidities. But it is difficult to figure out the exact quantity of each
nutrient because nutrients requirement varies based on physical and disease
conditions. In our study we proposed a novel machine learning based system to
predict the amount of nutrients one individual requires for being healthy. We
applied different machine learning algorithms: linear regression, support
vector machine (SVM), decision tree, random forest, XGBoost, LightGBM on fluid
and 3 other major micronutrients: carbohydrate, protein, fat consumption
prediction. We achieved high accuracy with low root mean square error (RMSE) by
using linear regression in fluid prediction, random forest in carbohydrate
prediction and LightGBM in protein and fat prediction. We believe our diet
recommender system, OBESEYE, is the only of its kind which recommends diet with
the consideration of comorbidities and physical conditions and promote
encouragement to get rid of obesity.