A Manifold Learning‐Based Anomaly Detection Framework for Cardiovascular Disease Diagnosis

ABSTRACT Cardiovascular diseases are the leading cause of morbidity and mortality worldwide, which requires accurate and reliable diagnostic methods. This paper introduces an effective anomaly detection framework, t‐SNE‐LOF, which leverages the complementary strengths of t‐distributed Stochastic Neighbor Embedding (t‐SNE) and Local Outlier Factor (LOF) detection. t‐SNE captures complex, nonlinear relationships within high‐dimensional data by reducing it to a lower‐dimensional manifold while preserving local and global structures, whereas LOF identifies anomalies based on density variations, making it well‐suited for detecting outliers in heterogeneous datasets. By combining these techniques, t‐SNE‐LOF effectively isolates cardiovascular anomalies with enhanced precision, achieving Area Under the Curve (AUC) scores of 99.28% and 98.75% on two publicly available datasets, outperforming state‐of‐the‐art baseline methods. Comparative evaluation against a broad range of classical semi‐supervised methods (iForest, LOF, EE), three embedding‐based variants, seven traditional supervised classifiers, and six deep learning models confirms the effectiveness and robustness of the approach. Additionally, SHapley Additive exPlanations (SHAP) analysis interprets the model's predictions, providing insights into feature contributions and enhancing the explainability of the t‐SNE‐LOF framework for cardiovascular anomaly detection. The proposed method is semi‐supervised and requires only healthy data during training, making it highly suitable for real‐world clinical settings where labeled abnormal cases are limited. Future work will explore perplexity‐free parametric t‐SNE to improve scalability and eliminate manual tuning, along with automated hyperparameter optimization and extension to multi‐modal cardiovascular data. This approach offers satisfactory performance and may support integration into Clinical Decision Support Systems to aid early cardiovascular diagnosis and prevention.