Immanuel D - Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore, Tamil Nadu, India.
Leo E - Computer Science and Engineering, Kings Engineering College, Chennai, Tamil Nadu, India.
S - Computer Science and Engineering, N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India.
C - Computer Science and Engineering, KIT-Kalaignar Karunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India.
K - Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India.
D - Computer Science and Engineering, M.K Umarasamy College of Engineering, Karur, Tamil Nadu, India.

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of ۱۸.۶M people in ۲۰۱۷. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of ۹۹.۶۲ percent. The sensitivity and specificity were likewise measured at ۹۹.۶۵% and ۹۸.۵۴%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.

Optimization Algorithm, cardiovascular disease, Prediction, Gradient Descent, Machine learning, neural networks, Deep learning