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AI-Powered Early Detection of Cardiovascular Diseases: A Global Health Priority
Authors
Md Shafiqul Islam 1 ,  Md Mesbah Uddin 2 ,  Md. Firoz Hossain 3 ,  Md Redwan Hussain 4 ,  Oli Ahammed Sarker 4 ,  Toufiqur Rahman Tonmoy 5 ,  Rokeya Khatun Shorna 5 ,  Abu Hanif 6
Affiliations

1 Department of Computer Science, Maharishi International University, 1000 North Fourth St., Fairfield, Iowa 52557, USA

2 Department of Occupational and Environmental Health, Bangladesh University of Health Science, 125, Technical Mor, 1 Darus Salam Rd, Dhaka 1216

3 Institute of Social Welfare and Research, University of Dhaka, Shahbag, Dhaka 1205, Bangladesh

4 Department of Computer Science and Engineering, Daffodil International University, Birulia, Savar, Dhaka-1216

5 Department of Computer Science and Engineering, Jahangirnagar University, Kalabagan Rd, Savar 1342, Dhaka, Bangladesh

6 Department of Business Administration, International American University, 10th Floor, #1000 ​Los Angeles, CA 90010, USA

Author's Details

Name: Md Shafiqul Islam

Email: shafiqswh@gmail.com

Department: Department of Computer Science

Affiliation Number: 1

Address: 1000 North Fourth St., Fairfield, Iowa 52557, USA

https://doi.org/10.103/xxx
Abstract
Timely identification of Cardiovascular diseases (CVDs) is critical in their prevention. However, conventional diagnostic techniques encounter challenges like late identification of the dangers and inadequate utilization of multiple risk factors. This work perfectly illustrates the possibilities of AI in improving the identification of CVD by integrating EHRs, imaging data, and data from wearable devices. An analysis involving a dataset of 50,000 patients developed and assessed AI models using three configurations: Electronic health record data, imaging data, and integrated data. This is also supported by the results of the integrated model, which had 92 percent accuracy with an AUC-ROC of 0.94, which added to the percent accuracy of single-source models. Multimodal data were used in the integrated model to assess the risk factors related to CVD, the changes in the patient’s physiology throughout the study, and the historical trends. It was also found that this type of diagnostics brings many clinical and societal benefits since it has better prediction accuracy, costs less, and leads to better patient outcomes.

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This article is Open Access CC BY-NC
Article Information
Article Type
Research Paper
Submitted
06 July, 2024
Revised
30 July, 2024
Accepted
14 August, 2024
Online First
22 August, 2024
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