Cardiovascular Disease Prediction with Diagnosis Information Using Deep Learning Algorithm

Abstract

Cardiovascular disease (CVD) is one of the most critical health problems affecting millions of people world- wide. According to global health reports, cardiovascular dis- eases account for a significant percentage of deaths every year. The increasing number of patients suffering from heart-related diseases highlights the need for early diagnosis and effective prediction systems. Early detection of cardiovascular diseases can help doctors provide timely treatment and prevent serious complications. Traditional diagnostic methods rely on medical examinations, electrocardiogram (ECG) interpretation, blood tests, and imaging techniques. Although these approaches are useful, they often require expert cardiologists to analyze medical reports. Manual interpretation of ECG signals can be time-consuming and some- times prone to human error. In addition, the growing volume of healthcare data makes it difficult for doctors to analyze every patient record efficiently. Recent advancements in artificial intelligence and deep learning have created new opportunities for improving medical diagnosis. Deep learning models can analyze large datasets and identify hidden patterns that may not be easily detected through conventional methods. These models are capable of learning complex relationships between medical features and disease conditions. This research proposes a deep learning-based system for predicting cardiovascular disease using patient diagnosis information and ECG data. The proposed system utilizes Convolutional Neural Networks (CNN) for extracting important features from ECG signals and Long Short-Term Memory (LSTM) networks for capturing sequential dependencies in medical data. The dataset used for this study contains several important attributes including age, blood pressure, cholesterol levels, glucose levels, body mass index, and ECG signals. Various data preprocessing techniques such as normalization, noise filtering, and data balancing are applied to enhance the quality of the dataset. Experimental results indicate that the proposed deep learning model provides improved prediction accuracy compared to traditional machine learning methods. The developed system can assist healthcare professionals in making faster and more reliable diagnostic decisions.