Paper Contents
Abstract
With the fast pace of modern life, patients need healthcare to be provided on time and accurately to increase patient activity. Conventional health systems are plagued by long queues, poor management of appointments, and delayed diagnosis caused by manual procedures and restricted access to specialists. Automated Symptom Analysis and Appointment Scheduler is an intelligent healthcare management system created with the aim of streamlining and optimizing the relationship between healthcare professionals and patients. By incorporating machine learning, secure data processing, and smart scheduling protocols, this system is intended to automate the major part of patient care from primary symptom reporting to prognosis prediction and specialist appointment scheduling.Patients are able to register, report their symptoms, and get automated initial diagnoses, while doctors can view patient information, validate diagnoses, suggest treatments, and schedule appointments. The intelligent symptom analysis engine of the system utilizes Python-based machine learning models to aid in determining possible medical conditions, helping to speed up and improve diagnosis. By means of automation and insights based on data, this platform aims to alleviate the burden from healthcare professionals, eliminate the waiting time in care, and enhance the overall effectiveness and efficiency of the healthcare process.The Automated Symptom Analysis and Appointment Scheduler initiative was a result of the imperative to update healthcare diagnosis and access through technology. Initially designed as a double-edged sword to assist patients and doctors alike, it went through many research, developmental, and testing phases. Underpinned by machine learning, the system now provides disease predictions and intelligent scheduling features. The work keeps advancing, with continued improvements to deliver greater accuracy, scalability, and connection to larger healthcare ecosystems.
Copyright
Copyright © 2025 Arun Kumar A N. This is an open access article distributed under the Creative Commons Attribution License.
