Utilizing AI: Early Diagnosis and Risk Assessment of Cardiovascular Diseases: The Emphasis on Machine Learning and Deep Learning

Decades of cardiovascular diseases have moved to the top of the list of global causes of mortality, calling for indepth assessment and management strategies to be implemented against these widespread health issues. Our research aims to explore various possible predictors and clinical characteristics concerning heart disorders. This is an in-depth study of several factors and risk determiners that may be attributed to cardiovascular diseases, exclusive of gender, age, cholesterol level, and blood pressure reads. Using state-of-the-art machine learning and deep learning techniques, we will run these variables through our model to uncover hidden patterns and build precise predictive models of the data set. The central goal of this research is to emphasize the need for proactive preventative measures and targeted interventions taken for individuals stratified as being at increased risk for heart disease. In this direction, we will use sophisticated predictive modeling techniques such as Neural Networks and Support Vector Machines to improve the accuracy of our forecasts. Our findings indicate various success rates for multiple algorithms: the KNN model obtained a superb accuracy of $87 \%$, and the rest of the models-SVM and DT obtained 79%. The Random Forest (RF) model had an accuracy of $\mathbf{8 4 \%}$. The Artificial Neural Network (ANN) and the Feedforward Neural Network (FFNN) obtained the most notable results with incredible accuracies of $\mathbf{9 2 \%}$ and $\mathbf{9 6 \%}$, respectively