Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

Area of Science:

• Artificial Intelligence in Healthcare
• Machine Learning for Disease Prediction

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly used in healthcare.
• ML models offer significant potential for predicting cardiovascular diseases (CVDs) using medical data.
• Despite advancements, numerous challenges can compromise the performance and clinical utility of these models.

Purpose of the Study:

• To identify and analyze common pitfalls in ML models for cardiovascular disease prediction.
• To propose solutions for improving data quality, model design, statistical methods, and clinical implications.
• To provide guidance for researchers, developers, policymakers, and clinicians in this rapidly evolving field.

Main Methods:

• Analysis of existing problems in data quality and dataset characteristics.
• Review of challenges in model design and statistical methodologies.
• Examination of clinical implications and evaluation criteria.

Main Results:

• Identified pitfalls span data quality, dataset characteristics, model design, statistical methods, and clinical integration.
• Highlighted the impact of these pitfalls on predictive performance, credibility, reliability, and reproducibility.
• Proposed solutions include objective data collection, improved training, larger sample sizes, and robust statistical techniques.

Conclusions:

• Addressing identified pitfalls is essential for enhancing the value and clinical applicability of ML models in cardiovascular disease prediction.
• Standardizing outcomes, evaluation criteria, and ensuring fairness and replicability are key recommendations.
• This work serves as a critical reference for advancing AI in cardiovascular healthcare.