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A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease
Prabu Pachiyannan1, Musleh Alsulami2, Deafallah Alsadie2
1Department of Computer Science, CHRIST, Bangalore 560029, India.
Insights
A new Cardiac Deep Learning Model (CDLM) accurately predicts newborn mortality risk from congenital heart disease (CHD). This machine learning approach identifies high-risk infants, enabling timely interventions to save lives globally.
Area of Science:
- Cardiology
- Medical Informatics
- Public Health
Background:
- Congenital heart disease (CHD) is a major cause of newborn mortality, disproportionately affecting low- and middle-income countries.
- Limited resources and healthcare access exacerbate CHD mortality rates in vulnerable populations.
- Machine learning offers a promising avenue for developing predictive models to assess CHD mortality risk.
Purpose of the Study:
- To propose an innovative machine learning approach to minimize newborn mortality associated with congenital heart disease.
- To develop a diagnostic model that identifies key risk factors for CHD mortality in newborns.
- To empower healthcare professionals with tools for customized interventions and early detection strategies.
Main Methods:
- Analysis of data from infants diagnosed with CHD, including maternal clinical history and fetal health information.
- Development and application of a Cardiac Deep Learning Model (CDLM) for risk prediction.
- Evaluation of the model's performance using metrics such as sensitivity, specificity, and predictive values.
Main Results:
- The proposed CDLM demonstrated high performance in predicting CHD mortality risk.
- Key performance metrics include: sensitivity of 91.74%, specificity of 92.65%, positive predictive value of 90.85%, and negative predictive value of 55.62%.
- The model achieved a miss rate of 91.03%, indicating its effectiveness in identifying at-risk infants.
Conclusions:
- The CDLM provides a powerful tool for healthcare professionals to combat CHD-related newborn mortality.
- Accurate risk prediction enables targeted interventions, including intensified care and early treatment.
- This research has the potential to significantly improve healthcare outcomes and save lives worldwide.
Abstract:
Congenital heart disease (CHD) is a critical global public health concern, particularly when it comes to newborn mortality. Low- and middle-income countries face the highest mortality rates due to limited resources and inadequate healthcare access. To address this pressing issue, machine learning presents an opportunity to develop accurate predictive models that can assess the risk of death from CHD. These models can empower healthcare professionals by identifying high-risk infants and enabling appropriate care. Additionally, machine learning can uncover patterns in the risk factors associated with CHD mortality, leading to targeted interventions that prevent or reduce mortality among vulnerable newborns. This paper proposes an innovative machine learning approach to minimize newborn mortality related to CHD. By analyzing data from infants diagnosed with CHD, the model identifies key risk factors contributing to mortality. Armed with this knowledge, healthcare providers can devise customized interventions, including intensified care for high-risk infants and early detection and treatment strategies. The proposed diagnostic model utilizes maternal clinical history and fetal health information to accurately predict the condition of newborns affected by CHD. The results are highly promising, with the proposed Cardiac Deep Learning Model (CDLM) achieving remarkable performance metrics, including a sensitivity of 91.74%, specificity of 92.65%, positive predictive value of 90.85%, negative predictive value of 55.62%, and a miss rate of 91.03%. This research aims to make a significant impact by equipping healthcare professionals with powerful tools to combat CHD-related newborn mortality, ultimately saving lives and improving healthcare outcomes worldwide.
