Early Cardiovascular Disease Detection: Ai Deep Learning Approach for Risk Stratification

Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality worldwide, emphasizing the need for early detection and risk stratification. This study employs artificial intelligence (AI) and deep learning techniques to develop a predictive model for assessing CVD risk based on data extracted from existing literature. A systematic review was conducted to collect publicly available datasets from peer-reviewed studies, including demographic, clinical, and biomarker information relevant to CVD risk assessment. A retrospective study design was utilized, where statistical and machine learning methodologies were applied to analyze pre-existing data. Feature selection was performed using logistic regression and principal component analysis (PCA) to identify the most significant predictors of CVD. A deep learning model incorporating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks was trained on the extracted dataset. The model's performance was evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Comparative analysis with traditional statistical models, such as Cox proportional hazards regression and logistic regression, was conducted to assess the added value of AI-driven approaches. Preliminary findings suggest that deep learning models achieve superior predictive performance compared to traditional statistical methods, with an AUC-ROC exceeding 0.90 in risk classification. These results highlight the potential of AI-driven risk stratification tools in enhancing early CVD detection. Future research should explore the clinical integration of such models to optimize patient outcomes.