Applying Multiple Feature selection strategies to Boost the Effectiveness of Machine Learning and Deep Learning Models for Angiography Outcome Predictions

Abstract

Abstract: Angiography serves as a vital imaging technique for diagnosing and treating cardiovascular disease by enabling precise visualization of coronary arteries. This study investigates the impact of multiple feature selection strategies on enhancing the predictive accuracy of machine learning (ML) and deep learning (DL) models in classifying angiography outcome. The dataset consists of 976 angiography videos, categorized into three classes: Normal, Coronary Artery Bypass Grafting (CABG), and Angioplasty. [1] To ensure data reliability, cases related to percutaneous Trans venous Mitral Commissurotomy (PTMC) and insufficient patient records were excluded. A Comparative analysis was conducted using traditional ML models such as Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) alongside deep learning architecture like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. [2] Various feature selection techniques, including Principle Component Analysis (PCA), Recursive Feature Elimination (RFE), and Mutual Information (MI), were employed to enhance model interpretability and efficiency. [3] Results demonstrate that while ML models achieved moderate accuracy ranging between 85% and 90% and DL models significantly outperformed them, reaching an accuracy of 91%. The superior performance of DL approaches can be attributed to their ability to automatically extract hierarchical spatial and temporal features from angiography videos, whereas ML models rely heavily on handcrafted features, limiting their effectiveness [4]. These findings emphasize the critical role of feature selection and deep learning methodologies in improving angiography outcome predictions, ultimately contributing to Al-driven advancements in cardiovascular diagnostics. The study underscores the potential of DL-based automated angiography assessment in supporting clinical decision-making, leading to improved patient management and early intervention strategies. [5]

More by Mohammad Hashmi

• Physics-Guided Machine Learning for FMCW Radar-Based Human and Metal Target ClassificationIEEE Sensors Journal · 2026
• A novel filtering T-shaped transmission line structure and its application in the design of reduced-size Butler matrixScientific Reports · 2026 · Citations: 1
• Textile Integrated MIMO Antenna with Distinct Elements for On/Off Body mmWave 5G WearablesUnited States National Committee of URSI National Radio Science Meeting · 2026 · Citations: 1
• Wireless Power and Information Transfer System Employing DGS based ResonatorsUnited States National Committee of URSI National Radio Science Meeting · 2026