Scholarlink

The combination of IoT and ML revolutionizes healthcare by enabling early kidney disease detection, redefining proactive patient care with precision and efficiency. Leveraging real-time data from IoT-enabled devices, ML algorithms analyze vital parameters, facilitating the identification of subtle risk factors. Personalized risk models, considering genetics and lifestyle, empower timely interventions. This innovative approach fosters continuous patient-healthcare provider communication, supporting collaborative, patient-centric care. The convergence of IoT and ML, underpinned by robust security measures, establishes a dynamic healthcare ecosystem. This proactive, data-driven model presents a significant stride toward precision medicine and personalized healthcare solutions.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Early Detection of Kidney Disease Risk Factors Through IoT-Enabled Machine Learning Systems

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • M. Ravi,
  • Pokala Krishnaiah,
  • Chilukuri Dileep,
  • B. Annapoorna,
  • M. Janga Reddy,
  • B. Satyanarayana

摘要

The combination of IoT and ML revolutionizes healthcare by enabling early kidney disease detection, redefining proactive patient care with precision and efficiency. Leveraging real-time data from IoT-enabled devices, ML algorithms analyze vital parameters, facilitating the identification of subtle risk factors. Personalized risk models, considering genetics and lifestyle, empower timely interventions. This innovative approach fosters continuous patient-healthcare provider communication, supporting collaborative, patient-centric care. The convergence of IoT and ML, underpinned by robust security measures, establishes a dynamic healthcare ecosystem. This proactive, data-driven model presents a significant stride toward precision medicine and personalized healthcare solutions.