Harnessing Biomedical Signals: A Modern Fusion of Hadoop Infrastructure, AI, and Fuzzy Logic in Healthcare

Authors

  • Vamsi Krishna Yarlagadda SAP Architect, Seattle School District, John Stanford Center for Educational Excellence, USA

Keywords:

Biomedical Signals, Hadoop Infrastructure, Artificial Intelligence (AI), Fuzzy Logic, Signal Fusion, Healthcare Informatics, Big Data in Healthcare

Abstract

This research investigates the combination of Hadoop infrastructure, artificial intelligence (AI), and fuzzy logic in analyzing biological signals. The goal is to improve the efficiency of data processing, accuracy of diagnosis, and management of uncertainty in healthcare. Secondary data, performance measurements, and case studies are analyzed to evaluate the technology. The significant results indicate that Hadoop's scalable architecture significantly decreases the time required for preprocessing, while AI approaches dramatically enhance the accuracy of diagnosis for different biological inputs. Fuzzy logic aids in managing ambiguity and produces interpretable outcomes, improving diagnostic accuracy. However, creating fuzzy logic rules, getting high-quality data, and using computer resources remain issues. The policy implications include a need for better sharing of data, more excellent professional training, and the creation of uniform integration procedures. These steps will enhance the widespread use of these sophisticated technologies, resulting in more precise and efficient interpretation of biological signals and eventually enhancing patient care and results.

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Published

2021-12-31

Issue

Vol. 8 No. 2 (2021): July - December Issue

Section

Peer-reviewed Article

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How to Cite

Yarlagadda, V. K. (2021). Harnessing Biomedical Signals: A Modern Fusion of Hadoop Infrastructure, AI, and Fuzzy Logic in Healthcare. Malaysian Journal of Medical and Biological Research, 8(2), 85-92. https://mjmbr.my/index.php/mjmbr/article/view/689

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