Leveraging Digital Biomarkers and Advanced Data Analytics in Medical Laboratory to Enhance Early Detection and Diagnostic Accuracy in Cardiovascular Diseases

Akuchinyere Titus Okpanachi, Igba Emmanuel, Paul Okugo Imoh, Noble Hendrix Dzakpasu and Mathew Nyaledzigbor

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 2, 468-495

Abstract: Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection and accurate diagnosis. This review explores the integration of digital biomarkers and advanced data analytics in medical laboratory settings to enhance the precision, efficiency, and predictive capabilities of cardiovascular diagnostics. Digital biomarkers, derived from wearable sensors, electronic health records (EHRs), and imaging technologies, provide real-time, non-invasive monitoring of physiological parameters such as heart rate variability, blood pressure trends, and biochemical markers. Coupled with advanced data analytics, including machine learning algorithms and predictive modeling, these biomarkers enable early risk stratification, automated anomaly detection, and personalized treatment strategies. The review examines the role of artificial intelligence (AI) and big data in refining diagnostic workflows, reducing false positives and negatives, and improving clinical decision-making. Additionally, it highlights challenges such as data privacy, interoperability, and regulatory compliance in implementing digital biomarker-driven diagnostics. By bridging the gap between emerging technologies and clinical practice, this study underscores the transformative potential of digital biomarkers and data-driven methodologies in revolutionizing cardiovascular disease detection and management.

Keywords: Digital biomarkers; Cardiovascular diagnostics; Artificial intelligence; Big data analytics; Predictive modeling; Healthcare interoperability (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST251222590 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST251222590/IJSRST251222590 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i2:id:690

DOI: 10.32628/IJSRST251222590

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().