Heart Disease Prediction System using Hybrid Deep Neural Network

Authors:

Prof. S. L. Farpat¹, Miss. Vaishnavi Bhople², Mr. Jayesh Jaiswal^3, Mr. Prasad Dandge⁴, Mr. Pratik ⁵,

¹ Professor, Department of Computer Science Engg,

² Students, Department of Computer Science Engg, Dr. V. B. Kolte College of Engineering, Malkapur, India

³ Students, Department of Computer Science Engg, Dr. V. B. Kolte College of Engineering, Malkapur, India

⁴ Students, Department of Computer Science Engg, Dr. V. B. Kolte College of Engineering, Malkapur, India

⁵Students, Department of Computer Science Engg, Dr. V. B. Kolte College of Engineering, Malkapur, India

Abstract:

Heart disease is one of the leading causes of mortality worldwide, making early detection and prevention critically important. The increasing availability of medical data and advancements in artificial intelligence have enabled the development of intelligent systems for disease prediction. This project presents a robust heart disease prediction system using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed system analyzes various clinical parameters such as age, blood pressure, cholesterol level, heart rate, and other relevant medical attributes to predict the likelihood of heart disease in patients.

Data preprocessing techniques are applied to ensure data quality, consistency, and reliability. The CNN component is used for extracting significant features from the input data, while the LSTM component captures temporal dependencies and sequential patterns in patient health records. The hybrid model improves prediction accuracy and overcomes the limitations of traditional machine learning methods. The system is trained and evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score.

The results demonstrate that the proposed hybrid model achieves higher accuracy and reliability compared to conventional approaches. This system can assist healthcare professionals in early diagnosis and decision-making, thereby reducing the risk of severe health complications. Although the system is not intended to replace medical experts, it serves as an effective support tool in the healthcare domain. Future enhancements may include real-time data integration, explainable AI techniques, and large-scale deployment in healthcare systems.

Keywords: - Clinical parameters, Convolutional Neural Networks (CNN) , Long Short-Term Memory (LSTM) networks, a accuracy, precision, recall, and F1-score.