PDF(Pubmed)
Abstract：
Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoT-based sensors for data collection, followed by data encoding and infectious risk factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the health-related information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out, and results are calculated based on real-time patient data. The statistical findings of accuracy (91.45%), specificity (95.96%), sensitivity (84.79%), precision (95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.
摘要：
尿液感染是医疗保健行业最普遍的问题之一，可能会损害肾脏和其他肾脏器官的功能。因此,此类感染的早期诊断和治疗对于避免未来的并发症至关重要.引人注目的是,在目前的工作中,提出了一种早期预测尿液感染的智能系统。拟议的框架使用基于物联网的传感器进行数据收集，然后在雾计算平台上使用XGBoost算法进行数据编码和感染危险因子计算。最后,分析结果与用户的健康相关信息一起存储在云存储库中，以备将来分析。对于性能验证，已经进行了广泛的实验，结果是根据实时患者数据计算出来的。统计结果的准确性（91.45%），特异性（95.96%），灵敏度（84.79%），精度（95.49%），和f分数（90.12％）表明，与其他基线技术相比，拟议策略的性能显着提高。
