The Heart Health Detector paper leverages advancements in artificial intelligence and machine learning to provide an accessible and user-friendly tool for monitoring and managing heart health. This innovative technology has the potential to revolutionize preventive healthcare by empowering individuals to take control of their cardiovascular well-being. By making heart health monitoring more accessible and user-friendly, the Heart Health Detector could lead to earlier detection of cardiac issues and improved patient outcomes. Furthermore, this tool may help reduce the burden on healthcare systems by promoting proactive heart health management and potentially decreasing the incidence of severe cardiac events. Cardiovascular diseases are a leading cause of death globally, and their early detection and management can significantly reduce risks and improve outcomes. This study bridges the gap between complex medical data and user-friendly health monitoring through a web application that offers personalized health insights, proactive heart health management, and simplified user experiences. It ensures data privacy and security, and encourages preventive health measures. The application uses GPT-4 to analyze user-provided health data using data analysis on two data files: a) hospital file and b) heart file, delivering personalized recommendations. It empowers users to take proactive steps toward optimizing their cardiovascular well-being, democratizing access to heart health information, and contributing to the prevention and management of heart diseases on a broader scale. The development involved meticulous conceptualization, data acquisition, model training, and validation, resulting in a sophisticated yet user-friendly platform that integrates advanced AI algorithms to analyze health metrics and provide actionable insights and recommendations. While artificial intelligence and machine learning offer promising opportunities for developing user-friendly heart health monitoring tools, they also present significant challenges in terms of data privacy, security, and the effective integration of complex medical information into accessible applications. The Heart Health Detector aims to bridge this gap by providing personalized insights and recommendations, yet it must carefully balance sophisticated AI algorithms with user-friendliness to ensure widespread adoption and impact. Although such tools have the potential to democratize access to heart health information and promote preventive measures, their limitations and challenges must be carefully considered to maximize their effectiveness and reliability in real-world healthcare settings.
| Published in | Automation, Control and Intelligent Systems (Volume 12, Issue 4) |
| DOI | 10.11648/j.acis.20241204.13 |
| Page(s) | 114-124 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Heart Health Detector, Custom GPT Using ChatGpt, Early Diagnosis, Geolocation, Data Analysis
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APA Style
Bansal, N., Singla, B. (2024). Heart Health Detector GPT Based on GPT-4o Model. Automation, Control and Intelligent Systems, 12(4), 114-124. https://doi.org/10.11648/j.acis.20241204.13
ACS Style
Bansal, N.; Singla, B. Heart Health Detector GPT Based on GPT-4o Model. Autom. Control Intell. Syst. 2024, 12(4), 114-124. doi: 10.11648/j.acis.20241204.13
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Download@article{10.11648/j.acis.20241204.13,
author = {Neha Bansal and Bhawna Singla},
title = {Heart Health Detector GPT Based on GPT-4o Model
},
journal = {Automation, Control and Intelligent Systems},
volume = {12},
number = {4},
pages = {114-124},
doi = {10.11648/j.acis.20241204.13},
url = {https://doi.org/10.11648/j.acis.20241204.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20241204.13},
abstract = {The Heart Health Detector paper leverages advancements in artificial intelligence and machine learning to provide an accessible and user-friendly tool for monitoring and managing heart health. This innovative technology has the potential to revolutionize preventive healthcare by empowering individuals to take control of their cardiovascular well-being. By making heart health monitoring more accessible and user-friendly, the Heart Health Detector could lead to earlier detection of cardiac issues and improved patient outcomes. Furthermore, this tool may help reduce the burden on healthcare systems by promoting proactive heart health management and potentially decreasing the incidence of severe cardiac events. Cardiovascular diseases are a leading cause of death globally, and their early detection and management can significantly reduce risks and improve outcomes. This study bridges the gap between complex medical data and user-friendly health monitoring through a web application that offers personalized health insights, proactive heart health management, and simplified user experiences. It ensures data privacy and security, and encourages preventive health measures. The application uses GPT-4 to analyze user-provided health data using data analysis on two data files: a) hospital file and b) heart file, delivering personalized recommendations. It empowers users to take proactive steps toward optimizing their cardiovascular well-being, democratizing access to heart health information, and contributing to the prevention and management of heart diseases on a broader scale. The development involved meticulous conceptualization, data acquisition, model training, and validation, resulting in a sophisticated yet user-friendly platform that integrates advanced AI algorithms to analyze health metrics and provide actionable insights and recommendations. While artificial intelligence and machine learning offer promising opportunities for developing user-friendly heart health monitoring tools, they also present significant challenges in terms of data privacy, security, and the effective integration of complex medical information into accessible applications. The Heart Health Detector aims to bridge this gap by providing personalized insights and recommendations, yet it must carefully balance sophisticated AI algorithms with user-friendliness to ensure widespread adoption and impact. Although such tools have the potential to democratize access to heart health information and promote preventive measures, their limitations and challenges must be carefully considered to maximize their effectiveness and reliability in real-world healthcare settings.
},
year = {2024}
}
TY - JOUR T1 - Heart Health Detector GPT Based on GPT-4o Model AU - Neha Bansal AU - Bhawna Singla Y1 - 2024/12/30 PY - 2024 N1 - https://doi.org/10.11648/j.acis.20241204.13 DO - 10.11648/j.acis.20241204.13 T2 - Automation, Control and Intelligent Systems JF - Automation, Control and Intelligent Systems JO - Automation, Control and Intelligent Systems SP - 114 EP - 124 PB - Science Publishing Group SN - 2328-5591 UR - https://doi.org/10.11648/j.acis.20241204.13 AB - The Heart Health Detector paper leverages advancements in artificial intelligence and machine learning to provide an accessible and user-friendly tool for monitoring and managing heart health. This innovative technology has the potential to revolutionize preventive healthcare by empowering individuals to take control of their cardiovascular well-being. By making heart health monitoring more accessible and user-friendly, the Heart Health Detector could lead to earlier detection of cardiac issues and improved patient outcomes. Furthermore, this tool may help reduce the burden on healthcare systems by promoting proactive heart health management and potentially decreasing the incidence of severe cardiac events. Cardiovascular diseases are a leading cause of death globally, and their early detection and management can significantly reduce risks and improve outcomes. This study bridges the gap between complex medical data and user-friendly health monitoring through a web application that offers personalized health insights, proactive heart health management, and simplified user experiences. It ensures data privacy and security, and encourages preventive health measures. The application uses GPT-4 to analyze user-provided health data using data analysis on two data files: a) hospital file and b) heart file, delivering personalized recommendations. It empowers users to take proactive steps toward optimizing their cardiovascular well-being, democratizing access to heart health information, and contributing to the prevention and management of heart diseases on a broader scale. The development involved meticulous conceptualization, data acquisition, model training, and validation, resulting in a sophisticated yet user-friendly platform that integrates advanced AI algorithms to analyze health metrics and provide actionable insights and recommendations. While artificial intelligence and machine learning offer promising opportunities for developing user-friendly heart health monitoring tools, they also present significant challenges in terms of data privacy, security, and the effective integration of complex medical information into accessible applications. The Heart Health Detector aims to bridge this gap by providing personalized insights and recommendations, yet it must carefully balance sophisticated AI algorithms with user-friendliness to ensure widespread adoption and impact. Although such tools have the potential to democratize access to heart health information and promote preventive measures, their limitations and challenges must be carefully considered to maximize their effectiveness and reliability in real-world healthcare settings. VL - 12 IS - 4 ER -
School of Computer Science and Engineering, Geeta University, Panipat, India
School of Computer Science and Engineering, Geeta University, Panipat, India
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