Diagnosis is a core part of both healthcare and care facilities, significantly reliant on techniques for the outcomes of patients. Traditional techniques usually are labor-intensive manual evaluations that tend to produce a clumsy and human error-prone process. These machine technologies improve the accuracy, efficiency, and availability of the diagnostic methods. With this project, the study aims to develop a web-based application that uses machine learning for medical diagnosis by analyzing patient symptoms and their medical history.The machine-learning models serve as the engine for the automated diagnostic assistant system harnessed through basic medical data interpretation phases. The web-based setup can be accessed by both patients andhealthcare professionals anytime and anywhere, allowing for distance troubleshooting and early detection of any disease. Data acquisition and preprocessing methods employed, model selection and evaluation metric are included in the research focus in order to ensure reliability and accuracy of the proposed system. Ultimately, it is expected that real-time diagnoses will be from this system, freeing medical professionals of the workload while supplementing their fidelity to the care of the patient.The primary areas of consideration included data privacy, data interpretation and practical applicability, all accompanied with a discussion on ethics in relation to patient data security and bias avoidance in machinelearning. The authors present results showing that a good machine learning model linked with a user-friendly web application would significantly improve accuracy and efficiency in diagnostics.
AI-drive, medical diagnostic, Machine Learning-Based Medical Diagnosis Web Application, Machine learning, CNN.
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