Balancing electrolytes is utmost important and essential for appropriate functioning of organs in human body as electrolytes imbalance can be an indication of the development of underlying pathophysiology. Efficient monitoring of electrolytes imbalance not only can increase the chances of early detection of disease, but also prevents the further deterioration of the health by strictly following nutrient controlled diet for balancing the electrolytes post disease detection. In this research, a recommender system MATURE Health is proposed and implemented, which predicts the imbalance of mandatory electrolytes and other substances presented in blood and recommends the food items with the balanced nutrients to avoid occurrence of the electrolytes imbalance. The proposed model takes user most recent laboratory results and daily food intake into account to predict the electrolytes imbalance. MATURE Health relies on MATURE Food algorithm to recommend food items as latter recommends only those food items that satisfy all mandatory nutrient requirements while also considering user past food preferences. To validate the proposed method, particularly sodium, potassium, and BUN levels have been predicted with prediction algorithm, Random Forest, for dialysis patients using their laboratory reports history and daily food intake. And, the proposed model demonstrates 99.53 percent, 96.94 percent and 95.35 percent accuracy for Sodium, Potassium, and BUN respectively. MATURE Health is a novel health recommender system that implements machine learning models to predict the imbalance of mandatory electrolytes and other substances in the blood and recommends the food items which contain the required amount of the nutrients that prevent or at least reduce the risk of the electrolytes imbalance.

提出并实现了一种基于机器学习的健康推荐系统 MATURE Health，该系统通过使用最近的实验室结果和日常饮食摄入来预测必需电解质和血液中其他物质的失衡并推荐所需的营养物质，以防止或至少减少电解质失衡的风险（其中特别预测了钠，钾和尿素氮水平，其预测准确率高达 99.53％，96.94％ 和 95.35％）。

MATURE-HEALTH: 必备功能选择的健康推荐系统