Convolutional neural network-based cardiovascular disease prediction with adaptability

Abstract

Cardiovascular diseases (CVD) are a leading cause of global mortality, as documented by the World Health Organization (WHO), resulting in a significant annual death toll. Accurate forecasting of CVD has the potential to significantly improve patient outcomes and potentially save lives. The advent of the COVID-19 pandemic has necessitated the adaptation of traditional disease prediction methodologies, underscoring the importance of a unified and precise predictive system. The integration of Artificial Intelligence (AI) in disease diagnosis and identification has been transformative, largely due to the rapid progress and reliability of deep learning technologies. This study proposes the application of Convolutional Neural Networks (CNNs) to the heart disease dataset from the University of California, Irvine (UCI) repository, employing machine learning (ML) techniques for the early detection of CVD. The analysis encompasses 14 critical attributes from the dataset. The performance of the proposed model is assessed using accuracy and confusion matrix evaluation metrics, which are employed to validate multiple prospective findings. To refine the dataset, the Isolation Forest algorithm is utilized to eliminate irrelevant features, and data standardization techniques are implemented to enhance the model's predictive accuracy. The application of deep learning methodologies has resulted in an impressive accuracy rate of 98%.