کنفرانس بین المللی محاسبات نرم

صفحه اصلی / ششمین کنفرانس بین المللی محاسبات نرم

Review of Neural Networks for Prediction and Diagnosis of Cardiovascular Diseases

نویسندگان :

Maryam Norouzzadeh Ravari¹ Soodeh Shadravan²

1- دانشگاه آزاد واحد کرمان 2- دانشگاه آزاد واحد بردسیر

کلمات کلیدی :

Heart disease diagnosis،Neural network،Perceptron،Convolution،LSTM،Artificial intelligence.

چکیده :

While the continuous recording of heart signals allows for timely monitoring, prediction, and alerting, heart diseases are among the leading causes of global mortality. Deep learning algorithms, capable of processing diverse inputs such as images, audio, and text, are utilized in various medical fields including disease diagnosis, health monitoring, and mortality prediction. The CNN-1D network achieved an accuracy of 99.2% and a sensitivity of 99.62% on the UCI dataset by extracting hierarchical features from time signals, while the MLP model reached an accuracy of 97.8% but had a low sensitivity of 56.45% in identifying true positive cases. The CNN+PSO method offered an accuracy of 92.34% with lower complexity, aiming to reduce dimensions. The CNN+Bi-LSTM model also provided a desirable accuracy in estimating heart rate with MAE=0.90. This review article evaluates the performance of models based on accuracy, sensitivity, and specificity indicators in a comparative table format and provides an analysis of the proposed methods.