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.

翻译：维持电解质平衡对人体器官正常运作至关重要，因为电解质失衡可能预示潜在病理变化的发展。有效监测电解质失衡不仅能提高疾病早期发现概率，还能通过严格遵循营养控制饮食来防止健康状况进一步恶化。本研究提出并实现了一种名为MATURE Health的推荐系统，该系统可预测血液中必需电解质及其他物质的失衡状况，并推荐营养均衡的食物以避免电解质失衡的发生。该模型基于用户最新的实验室检测结果和日常食物摄入量来预测电解质失衡。MATURE Health依赖MATURE Food算法进行食物推荐，该算法仅推荐满足所有必需营养素需求的食物，同时兼顾用户既往食物偏好。为验证所提方法，研究采用随机森林预测算法，利用透析患者的实验室报告历史及日常食物摄入数据，对其钠、钾及血尿素氮水平进行预测。实验结果表明，该模型对钠、钾、血尿素氮的预测准确率分别达到99.53%、96.94%和95.35%。MATURE Health作为一种新型健康推荐系统，通过机器学习模型预测血液中必需电解质及其他物质的失衡状况，并推荐含有适量营养素的食物，从而预防或至少降低电解质失衡的风险。