The Smart Analysis of Machine Learning-Based Diagnostics Model of Cardiovascular Diseases in Patients

An accurate way to identify and diagnose cardiovascular diseases in patients is to create a machine learning-based diagnostic tool called the Smart Analysis of Machine Learning-Based Diagnostics Model of Cardiovascular Diseases in Patients. It is based on the machine learning and used to classify cardiac conditions in real-time on the basis of electrocardiograms (ECGs) and other physiological data. After classification, the system then gives the physician the confidence level and the degree of certainty they should have in a decision. The project’s goal is to create a deep learning-based model that can increase accuracy even more. The model will be optimized to function with medical imaging data after being trained on a sizable dataset of ECGs and other physiological data. The group is employing a number of strategies, including convolution neural networks, deep networks, and recurrent networks, to accomplish this goal. In addition, they are investigating the application of contemporary optimization techniques and parallel computing to raise the system’s efficiency. In order to extract more meaningful insights from the data, the researchers are also creating fresh and enhanced knowledge representation and data mining methodologies.