CardioSense-A Deep Learning Enhanced Early Heart Disease Detection

Heart disease continues to be a significant reason for death globally, our project highlights the importance of efficient methods for detecting and intervening in the early stages. We use an assortment of cutting-edge bio-sensor technology with deep learning techniques to introduce a new system that predicts heart diseases at early stages. The suggested framework continually tracks the heart rate and sounds through a machine provided with advanced biosensors. These include Random Forest classifiers and Deformable Attention Transformers as well as Vision Transformers that process data collected by the sensors. The LSTM model recognizes temporal patterns in the physiological signals whereas the Random Forest classifier helps to choose reliable features for classification. This is enhanced by Deformable Attention Transformer which improves capability of this model to concentrate on important segments of data increasing its predictive accuracy. Our research findings suggest that this combined system provides an accurate and reliable way to detect heart diseases at earlier stages, which can be very useful in health care sector. The potential of integrating biological data with advanced machine learning algorithms for medical diagnoses is shown by the efficacy of our proposed method.