Heart disease prediction using image segmentation Through the CNN model

One of the most fatal diseases is heart disease. This is a condition that affects a big portion of the world’s population. When we examine the death rate and the enormous number of people who suffer from heart disease, it becomes clear how critical early detection of heart disease is. There are numerous established approaches for predicting such sickness, but they do not appear to be adequate. There is an immediate need for a medical diagnosis system that can anticipate heart disease early on and provide a more accurate diagnosis than standard approaches such as Logistic Regularization, Lasso, Elastic Network, and Group Lasso regularisation. Nowadays, machine learning approaches are gaining a lot of traction. Convolutional Neural Networks (CNNs) are utilised in this paper to create a system for early stage prediction and medical diagnosis. CNN receives 13 clinical features as input. The CNN is trained using a modified back propagation training approach. During testing, it was discovered that CNN predicts the absence and presence of cardiac disease with greater than 95 percent accuracy. In this investigation, we present CardioHelp, a method that uses a deep learning algorithm known as convolutional neural networks to predict whether or not a patient will have cardiovascular disease (CNN). In order to model temporal data, the suggested technique makes use of CNN for early HF prediction. We compiled a dataset on heart disease and used cutting-edge algorithms to compare our findings. The results were promising.