Leveraging Regression Analysis to Predict Overlapping Symptoms of Cardiovascular Diseases
نویسندگان
چکیده
In medical informatics, deep learning-based models are being used to predict and diagnose cardiovascular diseases (CVDs). These can detect clinical signs, recognize phenotypes, pick treatment methods for complicated illnesses. One approach predicting CVDs is collect a large dataset of patient records use it train learning model. This study investigated early prediction using regression analysis on 2621 from UAE hospitals, including age, symptoms, CVD information. We propose long short-term memory-based neural network by leveraging the analysis. It be seen that accuracy level increased when they were simulated in pairs one disease with another due overlapping symptoms. The study’s results suggest coronary heart has been predicted an 71.5% level, 84.4% Dyspnea; measured combination three conditions was 86.7%, Dyspnea, Chest Pain, Cyanosis, up 88.9%. Weakness, Fatigue, Emptysis showed value 89.8%. our proposed work, combinations Weakness Emptysis, discomfort pressure chest have shown ideal 90.6%, Fever, 91%. show effectiveness method several evaluation benchmarks.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3286311