Healthcare systems must adapt to the requirements of the digital era. The proposed healthcare Artificial Intelligence (AI) assistance provides a safe and user-friendly platform for physicians, patients, and administrators to meet their specific needs. The system’s architecture prioritizes user authentication and role-based access control to ensure that only authorized users have access to certain features. The technology allows patients to input their symptoms, which is the platform’s cornerstone offering. The technology uses a Machine Learning (ML) model and a large medical database to properly forecast probable illnesses based on the symptoms presented. This predictive feature helps individuals make educated decisions about their health and seek medical assistance proactively. The system’s creative approach extends to online consultations. Patients may seek consultations, schedule appointments, and conduct secure video chats from the comfort of their homes. This online consultation service offers a convenient and flexible option for medical treatment, especially for people with restricted mobility or wanting immediate assistance. This paper evaluates disease prediction using parameters like accuracy and confusion matrix performance. The neural network model performs better for the above parameters in comparison to the random forest and K-nearest neighbor ML models. The proposed system uses ML technology to deliver fast, accurate, and secure medical services, breaking down traditional healthcare barriers.

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

AI-Based Medical Assistance for Proactive Healthcare Predictions and Services

  • Vemula Harshith Sai,
  • Carolyn John,
  • S. Albin,
  • Shamanth Nagaraju,
  • Manu Elappila,
  • S. Athulya

摘要

Healthcare systems must adapt to the requirements of the digital era. The proposed healthcare Artificial Intelligence (AI) assistance provides a safe and user-friendly platform for physicians, patients, and administrators to meet their specific needs. The system’s architecture prioritizes user authentication and role-based access control to ensure that only authorized users have access to certain features. The technology allows patients to input their symptoms, which is the platform’s cornerstone offering. The technology uses a Machine Learning (ML) model and a large medical database to properly forecast probable illnesses based on the symptoms presented. This predictive feature helps individuals make educated decisions about their health and seek medical assistance proactively. The system’s creative approach extends to online consultations. Patients may seek consultations, schedule appointments, and conduct secure video chats from the comfort of their homes. This online consultation service offers a convenient and flexible option for medical treatment, especially for people with restricted mobility or wanting immediate assistance. This paper evaluates disease prediction using parameters like accuracy and confusion matrix performance. The neural network model performs better for the above parameters in comparison to the random forest and K-nearest neighbor ML models. The proposed system uses ML technology to deliver fast, accurate, and secure medical services, breaking down traditional healthcare barriers.