A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease
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 pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/13/2195 |
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| author | Prabu Pachiyannan Musleh Alsulami Deafallah Alsadie Abdul Khader Jilani Saudagar Mohammed AlKhathami Ramesh Chandra Poonia |
| author_facet | Prabu Pachiyannan Musleh Alsulami Deafallah Alsadie Abdul Khader Jilani Saudagar Mohammed AlKhathami Ramesh Chandra Poonia |
| author_sort | Prabu Pachiyannan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:44:04Z |
| format | Article |
| id | doaj.art-7cafaab58eff41039a04c7d28fc049cc |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T01:44:04Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-7cafaab58eff41039a04c7d28fc049cc2023-11-18T16:21:20ZengMDPI AGDiagnostics2075-44182023-06-011313219510.3390/diagnostics13132195A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart DiseasePrabu Pachiyannan0Musleh Alsulami1Deafallah Alsadie2Abdul Khader Jilani Saudagar3Mohammed AlKhathami4Ramesh Chandra Poonia5Department of Computer Science, CHRIST, Bangalore 560029, IndiaInformation Systems Department, Umm Al-Qura University, Makkah 21961, Saudi ArabiaInformation Systems Department, Umm Al-Qura University, Makkah 21961, Saudi ArabiaInformation Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaInformation Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaDepartment of Computer Science, CHRIST, Bangalore 560029, IndiaCongenital 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.https://www.mdpi.com/2075-4418/13/13/2195newbornmortalitycongenital heart diseasemachine learningheart diseasehealthcare |
| spellingShingle | Prabu Pachiyannan Musleh Alsulami Deafallah Alsadie Abdul Khader Jilani Saudagar Mohammed AlKhathami Ramesh Chandra Poonia A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease Diagnostics newborn mortality congenital heart disease machine learning heart disease healthcare |
| title | A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease |
| title_full | A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease |
| title_fullStr | A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease |
| title_full_unstemmed | A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease |
| title_short | A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease |
| title_sort | cardiac deep learning model cdlm to predict and identify the risk factor of congenital heart disease |
| topic | newborn mortality congenital heart disease machine learning heart disease healthcare |
| url | https://www.mdpi.com/2075-4418/13/13/2195 |
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