Medical Analysis Laboratory Management - MALM -

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dc.contributor.authorOmar K. Nasser
dc.contributor.authorMousa Al-Mahdi
dc.date.accessioned2024-02-28T09:34:02Z
dc.date.available2024-02-28T09:34:02Z
dc.date.issued2024-02-05
dc.description.abstractAbstract: This documentation reveals a system designed to improve efficiency and collaboration within medical laboratories. By taking a user-centric approach, MALM meets the needs of administrators, staff, patients and doctors, streamlining workflow and communications in medical analysis laboratories. This comprehensive guide walks through MALM features, defining user roles, functional requirements, and non-functional aspects. The integration of machine learning, specifically the Random Forest algorithm, enables doctors to predict a patient's condition based on test results. UML diagrams and detailed development process highlight the structure and evolution of the system. MALM is a scalable, secure and responsive solution to meet the evolving needs of healthcare practitioners and patients.
dc.identifier.urihttps://hdl.handle.net/20.500.11888/18664
dc.language.isoen_US
dc.supervisorDr. Baker Abdulhaq
dc.titleMedical Analysis Laboratory Management - MALM -
dc.typeGraduation Project
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