Automated Symptom Tracking And Prediction Of Angioedema Disease Using Machine Learning

The timely and precise identification of skin diseases is essential to optimizing therapy effectiveness and attaining favorable patient outcomes. However depending solely on visual examination and conventional diagnostic techniques adds subjectivity and might take a long time, which increases the risk of misdiagnosis or postponed treatment. This research aims to develop a machine-learning model to detect angioedema disease, a potentially fatal illness marked by sudden swelling of the skin and mucous membranes. Using several feature extraction techniques, the proposed model extracts texture, color, and intensity information from skin photographs. These extracted attributes are then integrated into a transfer learning framework to enhance overall performance, utilizing data from previously trained models. The shortcomings of traditional machine learning methods, such the need for huge datasets or protracted training cycles, are addressed by this innovative methodology The created approach has the potential to help medical practitioners make well-informed diagnoses, especially in areas with limited resources. The methodology has the potential to greatly improve patient outcomes and save healthcare costs by enabling early diagnosis and permitting proactive treatment actions. This development is especially helpful for increasing access to precise diagnoses in a variety of healthcare environments, guaranteeing that patients receive prompt and effective therapy.