Medical Diagnosis for Coronary Arteries Disease Detection Using Deep Learning

Abstract: Coronary Artery Disease (CAD) is among the most widespread and life-threatening cardiovascular conditions worldwide, responsible for a significant proportion of global mortality. Early and accurate detection of CAD plays a crucial role in enabling timely medical intervention and reducing the risk of severe cardiac complications. Conventional diagnostic approaches rely heavily on manual clinical analysis performed by experienced cardiologists, which is time- consuming, expensive, and susceptible to human error. This paper presents a Deep Learning-Based Coronary Artery Disease Detection System using a hybrid Convolutional Neural Network and Artificial Neural Network (CNN-ANN) architecture integrated with a Flask-based clinical web interface. The proposed system analyzes patient medical parameters from the UCI Heart Disease Dataset including age, blood pressure, cholesterol level, ECG reports, maximum heart rate, chest pain type, fasting blood sugar, and exercise-induced angina. The dataset undergoes systematic preprocessing involving normalization, label encoding, and an 80:20 train-test split. The Deep Learning model is implemented using TensorFlow and Keras with Dense layers, ReLU and Sigmoid activation functions, Dropout regularization, and Adam optimizer. A complementary module, CAD Vision Pro, extends the system to image-based coronary analysis using CCTA imaging with Grad-CAM heatmap visualization, risk scoring, severity classification, and automated PDF report generation. Experimental results demonstrate that the proposed system achieves approximately 95% prediction accuracy with high precision, recall, and F1-score values. The system provides healthcare professionals with an intelligent, automated, and user-friendly CAD detection platform that supports early diagnosis, reduces manual workload, and contributes toward AI-driven clinical decision support in modern healthcare.

Keywords: Coronary Artery Disease, Deep Learning, CNN, ANN, TensorFlow, Keras, Flask, UCI Heart Disease Dataset, CAD Vision Pro, Medical Image Analysis, Healthcare AI, CCTA, Grad-CAM