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Identifying at-risk patients for congenital heart disease using integrated predictive models and fuzzy clustering
Amirreza Salehi1, Majid Khedmati1
1Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
Insights
Machine learning and decision-making techniques improve congenital heart disease (CHD) prediction by 8%. The study highlights lifestyle, income, and maternal factors as key predictors for early CHD risk assessment.
Area of Science:
- Cardiology
- Computational Biology
- Public Health
Background:
- Congenital heart disease (CHD) affects ~1% of newborns globally, with complex genetic and environmental causes.
- Accurate diagnosis and forecasting of CHD remain challenging, particularly with imbalanced datasets.
Purpose of the Study:
- To develop a comprehensive prediction framework for CHD diagnostics and forecasting using Machine Learning (ML) and Multi-Attribute Decision Making (MADM).
- To enhance predictive accuracy and identify high-risk patient clusters for personalized risk evaluation.
Main Methods:
- Utilized supervised and unsupervised ML for data noise reduction and handling imbalanced datasets.
- Employed imbalance ensemble methods and K-means clustering for enhanced predictive accuracy.
- Applied Multi-Attribute Decision Making (MADM) and fuzzy clustering for risk assessment and stratification.
Main Results:
- Achieved an 8% improvement in recall compared to existing literature.
- Identified key predictors including unhealthy lifestyle, income, nutrition, folic acid, environmental factors, and maternal illnesses.
- Successfully clustered patients by risk level and assessed individual risk degrees.
Conclusions:
- The proposed ML/MADM framework significantly improves CHD prediction accuracy and risk stratification.
- Socioeconomic and lifestyle factors are critical determinants of CHD risk, necessitating integrated assessment.
- The framework facilitates early identification and intervention, potentially reducing the global burden of CHD.
Abstract:
Congenital heart disease (CHD) remains a significant global health concern, affecting approximately 1 % of newborns worldwide. While its accurate causes often remain elusive, a combination of genetic and environmental factors is implicated. In this cross-sectional study, we propose a comprehensive prediction framework leveraging Machine Learning (ML) and Multi-Attribute Decision Making (MADM) techniques to enhance CHD diagnostics and forecasting. Our framework integrates supervised and unsupervised learning methodologies to remove data noise and address imbalanced datasets effectively. Through the utilization of imbalance ensemble methods and clustering algorithms such as K-means, we enhance predictive accuracy, particularly in non-clinical datasets where imbalances are prevalent. Our results demonstrate an improvement of 8 % in recall compared to existing literature, showcasing the efficacy of our approach. Moreover, our framework identifies clusters of patients at the highest risk using MADM techniques, providing insights into susceptibility to CHD. Fuzzy clustering techniques further assess the degree of risk for individuals within each cluster, enabling personalized risk evaluation. Importantly, our analysis reveals that unhealthy lifestyle factors, annual per capita income, nutrition, and folic acid supplementation emerge as crucial predictors of CHD occurrences. Additionally, environmental risk factors and maternal illnesses significantly contribute to the predictive model. These findings underscore the multifactorial nature of CHD development, emphasizing the importance of considering socioeconomic and lifestyle factors alongside medical variables in CHD risk assessment and prevention strategies. Our proposed framework offers a promising avenue for early identification and intervention, potentially mitigating the burden of CHD on affected individuals and healthcare systems globally.
