cs.AIMar 6, 2026

Enhancing the Detection of Coronary Artery Disease Using Machine Learning

Authors: Karan Kumar Singh, Nikita Gajbhiye, Gouri Sankar Mishra

Organizations: Department of Computer Science and Engineering Sharda University, Greater Noida, India

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have shown significant potential in enhancing the accuracy of CAD diagnosis. This study investigates the application of ML algorithms to improve the detection of CAD by analyzing patient data, including clinical features, imaging, and biomarker profiles. Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and a hybrid of Bi-LSTM+GRU were trained on large datasets to predict the presence of CAD. Results demonstrated that these ML models outperformed traditional diagnostic methods in sensitivity and specificity, offering a robust tool for clinicians to make more informed decisions. The experimental results show that the hybrid model achieved an accuracy of 97.07%. By integrating advanced data preprocessing techniques and feature selection, this study ensures optimal learning and model performance, setting a benchmark for the application of ML in CAD diagnosis. The integration of ML into CAD detection presents a promising avenue for personalized healthcare and could play a pivotal role in the future of cardiovascular disease management.

Figures & tables

Author [Reference]Data Collection (No. of patients)MethodologyOutcomes
Ma et al., [16]Southwest China (300,000)XG-Boost, RF, and LRThe AUC for the suggested model was 0.70.
Mijwil et al., [18]UC Irvine ML repository (300)MLP, RF, SVM, KNN, LR, NB, and DTThe MLP algorithm reached an accuracy of over 88%.
Hammoud et al., [19]1190LR, SVM, KNN, RF, DT, NB, and GBThe RF algorithm achieved the highest accuracy rates of 89.50% before tuning and 94.96% after tuning.
Asif et al., [20]Heart disease datasetRF, GB, and XG-BoostThe ensemble model attained remarkable results in terms of Sensitivity (96.37%), Accuracy (94.53%), and Precision (98.37%).
Lin et al., [21]MIMIC-IV (5757)Cat-BoostThe suggested model had the strongest predictive performance, with an AUC of 0.760 and a training set of 0.831.
Yilmaz et al., [22]Heart Disease Dataset (11)SVM and RFThe SVM model achieved Specificity of 0.844, Sensitivity of 0.971, NPV of 0.887, PPV of 0.976, and F1-score of 0.816.
Table 1: Summary of the outcomes achieved by current ML-based approaches
Figure 1: Block diagram of proposed methodology
Figure 2: Training and validation loss of the Bi-LSTM model across epochs
Figure 3: Training and validation accuracy of the Bi-LSTM model across epochs
Figure 4: Confusion matrix of the Bi-LSTM model
ParametersValue (%)
Accuracy92.7
Precision92.9
Recall92.7
F1-score92.7
Table 2: Evaluation of Bi-LSTM
Figure 5: Training and validation loss of the GRU model across epochs
Figure 6: Training and validation accuracy of the GRU model across epochs
Figure 7: Confusion matrix of the GRU model
ParametersValue (%)
Accuracy93.9
Precision94
Recall93.9
F1-score93.8
Table 3: Evaluation of GRU
Figure 8: Accuracy of Hybrid Model
Figure 9: Loss of Hybrid Model
Figure 10: Confusion Matrix of Hybrid Model
ParametersValue (%)
Accuracy97.07
Precision94.13
Recall94.07
F1-score94
Table 4: Evaluation of the Hybrid Model
Author (Year) [Reference]MethodologyAccuracy (%)
Tang et al., (2023) [38]CNN70
Sapra et al., (2023) [39]DNN95.2
Choi et al., (2023) [40]Ob-CAD63.8
Our WorkHybrid Model97.07
Table 5: Evaluation of existing models compared to the proposed model
Figure 11: Comparison of previous work v/s present work

Explore similar work

CardsList

  1. Transforming Heart Disease Prediction with Advanced Machine Learning Techniques

    Aug 19, 2026Sami Ullah, Muhammad Mohsin KhanClinical Risk Prediction

    City University of Science in Information Technology, Peshawar 25000, Pakistan

  2. LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

    Apr 21, 2026Jeba Maliha, Md Rafiul KabirMedical LLMsClinical Risk Prediction

    Central Michigan University, Mount Pleasant, MI, USA

  3. Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans

    May 20, 2026Juhwan Lee, Ammar Hoori, Tao Hu +7RadiomicsComputed Tomography

    Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA · Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA · Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA +2