Prediction of Congenital Cardiovascular Disease Using Machine Learning Techniques: A Review Analysis
Keywords:Cardiovascular disease(CVD), Machine Learning (ML), Decision Tree, Random Forest, Support vector machines, Logistic regression, Deep learning, Electronic health records
Congenital cardiovascular disease (CVD) is a significant health concern affecting individuals from birth and often necessitating long-term medical management. Early prediction and diagnosis of CVD play a crucial role in improving patient outcomes and guiding appropriate interventions. In recent years, machine learning (ML) techniques have emerged as promising tools for CVD prediction, leveraging their ability to analyze complex patterns within large datasets. This review analysis explores the landscape of ML techniques employed to predict congenital CVD. Machine learning techniques have shown significant potential for cardiac disease prediction, especially when using large and complex datasets. This review paper comprehensively overviews several machine-learning methods for heart disease prediction. This research outlines the advantages and disadvantages of several machine learning techniques. It thoroughly analyzes their performance in predicting heart disease, conducting a comprehensive survey of recent literature encompassing diverse ML algorithms such as decision trees, support vector machines, random forests, neural networks, and deep learning architectures. Examining the various data sources utilized, including clinical records, genetic information, imaging data, and multi-omics data, highlighting their relevance and impact on prediction accuracy. Additionally, the performance metrics and evaluation strategies are employed in different studies to assess the predictive capabilities of the ML models. Lastly, providing insights into potential future directions, emphasizing the importance of collaborative efforts, standardized datasets, and robust validation methodologies. This review analysis aims to provide a comprehensive overview of the current state-of-the-art ML-based prediction of congenital CVD, highlighting its potential to revolutionize clinical practice and improve patient outcomes.