Abstract:
Cardiovascular diseases are the leading causes of death worldwide and the number of people dying from
cardiovascular disease is steadily increasing. The rapid economic transformation leading to environmental changes
and unhealthy lifestyles increase the risk factors and incidence of cardiovascular disease. The limited access to health
facilities, lack of expert cardiologists, and lack of regular health check-up trends make CVD the silent killers in low resource settings. Computer-aided diagnosis using Artificial intelligence techniques (AI) can help reduce the
mortality rate due to heart disease by providing decision support to experts allowing early diagnosis and treatment.
In this paper, an AI-based system has been proposed for the diagnosis of cardiovascular diseases using clinical data,
patient information, and electrocardiogram (ECG) signal. The proposed system includes an ECG processor part that
allows cardiologists to process and analyze the different waveforms, a machine learning-based heart disease
prediction system based on patient information and clinical data, and a deep learning-based 18 heart conditions
multiclass classification system using a 12-lead ECG signal. A user-friendly user interface has been also developed
for ease of use of the proposed techniques. The developed AI-based system was found to be 100% accurate in
predicting health disease based on clinical and patient information, and 93.27% accurate, on average, classifying
heart conditions based on a 12-lead ECG signal. The ECG processor also simplifies the analysis of important ECG
waveforms and segments. The experimental results indicate that the proposed system may have the potential for
facilitating heart disease diagnosis. The proposed method allows physicians to analyze and predict heart disease
easily and early, based on the available resource, improving diagnosis accuracy and treatment planning.
