Pachiyannan Prabu, Alsulami Musleh, Alsadie Deafallah, Saudagar Abdul Khader Jilani, AlKhathami Mohammed, Poonia Ramesh Chandra
Department of Computer Science, CHRIST, Bangalore 560029, India.
Information Systems Department, Umm Al-Qura University, Makkah 21961, Saudi Arabia.
Diagnostics (Basel). 2023 Jun 28;13(13):2195. doi: 10.3390/diagnostics13132195.
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.
先天性心脏病（CHD）是一个至关重要的全球公共卫生问题，尤其是在新生儿死亡率方面。由于资源有限和医疗保健服务不足，低收入和中等收入国家面临着最高的死亡率。为了解决这一紧迫问题，机器学习为开发准确的预测模型提供了机会，这些模型可以评估先天性心脏病导致的死亡风险。这些模型可以通过识别高危婴儿并提供适当的护理来增强医疗保健专业人员的能力。此外，机器学习可以揭示与先天性心脏病死亡率相关的风险因素中的模式，从而导致有针对性的干预措施，预防或降低脆弱新生儿的死亡率。本文提出了一种创新的机器学习方法，以尽量减少与先天性心脏病相关的新生儿死亡率。通过分析被诊断患有先天性心脏病的婴儿的数据，该模型识别出导致死亡的关键风险因素。有了这些知识，医疗保健提供者可以制定定制的干预措施，包括对高危婴儿加强护理以及早期检测和治疗策略。所提出的诊断模型利用母亲的临床病史和胎儿健康信息来准确预测受先天性心脏病影响的新生儿的状况。结果非常有前景，所提出的心脏深度学习模型（CDLM）取得了显著的性能指标，包括灵敏度为91.74%、特异度为92.65%、阳性预测值为90.85%、阴性预测值为55.62%以及漏诊率为91.03%。这项研究旨在通过为医疗保健专业人员提供强大的工具来对抗与先天性心脏病相关的新生儿死亡率，从而产生重大影响，最终拯救生命并改善全球的医疗保健结果。
