The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights

TL;DR

This paper systematically reviews recent machine learning approaches for congenital heart disease detection, analyzing datasets, algorithms, and challenges to advance early diagnosis and understanding.

Contribution

It provides a comprehensive meta-analysis of 432 references, highlighting key datasets, algorithms, and insights in ML-based congenital heart disease recognition from 2018 to 2024.

Findings

Identification of key datasets used in ML studies

Analysis of prevalent algorithms for detection

Highlighting challenges and future opportunities

Abstract

Congenital heart disease is among the most common fetal abnormalities and birth defects. Despite identifying numerous risk factors influencing its onset, a comprehensive understanding of its genesis and management across diverse populations remains limited. Recent advancements in machine learning have demonstrated the potential for leveraging patient data to enable early congenital heart disease detection. Over the past seven years, researchers have proposed various data-driven and algorithmic solutions to address this challenge. This paper presents a systematic review of congential heart disease recognition using machine learning, conducting a meta-analysis of 432 references from leading journals published between 2018 and 2024. A detailed investigation of 74 scholarly works highlights key factors, including databases, algorithms, applications, and solutions. Additionally, the survey outlines reported datasets used by machine learning experts for congenital heart disease recognition. Using a systematic literature review methodology, this study identifies critical challenges and opportunities in applying machine learning to congenital heart disease.