International Conference on Generative Artificial Intelligence, Cryptography, and Predictive Analytics

Abstract

Health is the primary asset of any nation that no one can afford to ignore. The focus concentrates on the essential issue of identifying and predicting certain cardiac diseases, such as congestive heart failure and coronary artery disease. The naïve effort toward a new era of early disease diagnosis. Preserving the integrity of cardiac health along with advanced technical algorithms is an urgent need. The research aims to provide a new paradigm for the early detection of diseases by improving diagnostic precision according to individual patients' needs. The chapter discusses the adoption of algorithms in health databases, tuning the platform's infrastructure and network for smooth communication. It will develop interfaces that users can use for diagnosis and tests, which will be quantitatively optimized constantly to reduce human mistakes. The study focuses on innovative multimodal machine learning algorithms that involve computer algorithms and machine learning algorithms such as Random Forest (RF), Logistic Regression (LR), and Support Vector Machines (SVM) with a fusion of classification techniques to portray the cardiac health space. This in turn increases the precision and stability of diagnostic criteria, which in turn improves early detection of cardiovascular diseases that are more accurate and reliable. Diverse clinical datasets were used to train our machine learning models, which proved to be highly accurate in diagnosing congestive heart failure (85.2%) and coronary artery disease (86.8%).