An Intelligent Web-Based Medical Management and Prescription Recommendation System Using Flask, MySQL, and Machine Learning
Keywords:
Emergency services management, Flask, Healthcare informatics, Machine learning, Medical management system, MySQL, Prescription recommendation, Role-based access controlAbstract
The healthcare sector continues to struggle with coordinating appointment scheduling, prescription management, patient feedback, and emergency-service delivery across disconnected, often manual, workflows. Addressing these issues, this study reports on the formulation, construction, and testing of an intelligent medical management system, a Flask-based web application supported by a MySQL database that brings together the four main users (patient, doctor, druggist, and administrator) in one single, secure, and role-based platform. The system is equipped with a supervised machine-learning component that suggests medications and personalised diet plans based on patient-reported symptoms as well as historical prescription data. Beyond that, the system has modules for appointment scheduling, prescription tracking, emergency-resource management, and centralised feedback handling. The system’s architecture is explained with the help of use-case activity sequence, data-flow, and deployment diagrams, and the system’s implementation is assessed via unit integration system, security, and user-acceptance testing, plus quantitative performance benchmarking and a comparative analysis of machine-learning models for the prescription recommendation task. The experimental evidence shows that the system developed is capable of matching the recommendation accuracy of baseline classifiers, while at the same time it greatly decreases administrative workload against traditional, isolated medical-record systems.
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