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.

Downloads

Download data is not yet available.

References

Addimulam, S., Mohammed, M. A., Karanam, R. K., Ying, D., Pydipalli, R., Patel, B., Shajahan, M. A., Dhameliya, N., & Natakam, V. M. (2020). Deep Learning-Enhanced Image Segmentation for Medical Diagnostics. Malaysian Journal of Medical and Biological Research, 7(2), 145-152. https://mjmbr.my/index.php/mjmbr/article/view/687

Akwei-Sekyere, S. (2015). Powerline Noise Elimination in Biomedical Signals via Blind Source Separation and Wavelet Analysis. PeerJ. https://doi.org/10.7717/peerj.1086

Anumandla, S. K. R., Yarlagadda, V. K., Vennapusa, S. C. R., & Kothapalli, K. R. V. (2020). Unveiling the Influence of Artificial Intelligence on Resource Management and Sustainable Development: A Comprehensive Investigation. Technology & Management Review, 5, 45-65. https://upright.pub/index.php/tmr/article/view/145

Das, S., Guha, D., Dutta, B. (2016). Medical Diagnosis with the Aid of Using Fuzzy Logic and Intuitionistic Fuzzy Logic. Applied Intelligence, 45(3), 850-867. https://doi.org/10.1007/s10489-016-0792-0

Duellmann, D., Surdy, K., Menichetti, L., Toebbicke, R. (2017). Hadoop and Friends - First Experience at CERN with A new Platform for High Throughput Analysis Steps. Journal of Physics: Conference Series, 898(7). https://doi.org/10.1088/1742-6596/898/7/072034

Elouaham, S., Latif, R., Dliou, A., Laaboubi, M., Maoulainie, F. M. R. (2013). Parametric and Non Parametric Time-Frequency Analysis of Biomedical Signals. International Journal of Advanced Computer Science and Applications, 4(1). https://doi.org/10.14569/IJACSA.2013.040110

Gacek, A., Pedrycz, W. (2015). Erratum to: Description, Analysis, and Classification of Biomedical Signals: A Computational Intelligence Approach. Soft Computing, 19(10), 3029-3029. https://doi.org/10.1007/s00500-015-1785-3

Gacek, A., Pedrycz, W. (2013). Description, Analysis, and Classification of Biomedical Signals: A Computational Intelligence Approach. Soft Computing, 17(9), 1659-1671. https://doi.org/10.1007/s00500-012-0967-5

Kothapalli, K. R. V. (2019). Enhancing DevOps with Azure Cloud Continuous Integration and Deployment Solutions. Engineering International, 7(2), 179-192.

Luo, Y., Hargraves, R. H., Belle, A., Bai, O., Qi, X. (2013). A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis. The Scientific World Journal, 2013. https://doi.org/10.1155/2013/896056

Manilo, L. A., Nemirko, A. P. (2016). Recognition of Biomedical Signals Based on Their Spectral Description Data Analysis. Pattern Recognition and Image Analysis, 26(4), 782-788. https://doi.org/10.1134/S1054661816040088

Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017). Machine Learning-Based Real-Time Fraud Detection in Financial Transactions. Asian Accounting and Auditing Advancement, 8(1), 67–76. https://4ajournal.com/article/view/93

Mohammed, M. A., Mohammed, R., Pasam, P., & Addimulam, S. (2018). Robot-Assisted Quality Control in the United States Rubber Industry: Challenges and Opportunities. ABC Journal of Advanced Research, 7(2), 151-162. https://doi.org/10.18034/abcjar.v7i2.755

Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142

Mullangi, K., Anumandla, S. K. R., Maddula, S. S., Vennapusa, S. C. R., & Mohammed, M. A. (2018). Accelerated Testing Methods for Ensuring Secure and Efficient Payment Processing Systems. ABC Research Alert, 6(3), 202–213. https://doi.org/10.18034/ra.v6i3.662

Mullangi, K., Yarlagadda, V. K., Dhameliya, N., & Rodriguez, M. (2018). Integrating AI and Reciprocal Symmetry in Financial Management: A Pathway to Enhanced Decision-Making. International Journal of Reciprocal Symmetry and Theoretical Physics, 5, 42-52. https://upright.pub/index.php/ijrstp/article/view/134

Nizamuddin, M., Natakam, V. M., Sachani, D. K., Vennapusa, S. C. R., Addimulam, S., & Mullangi, K. (2019). The Paradox of Retail Automation: How Self-Checkout Convenience Contrasts with Loyalty to Human Cashiers. Asian Journal of Humanity, Art and Literature, 6(2), 219-232. https://doi.org/10.18034/ajhal.v6i2.751

Richardson, N., Pydipalli, R., Maddula, S. S., Anumandla, S. K. R., & Vamsi Krishna Yarlagadda. (2019). Role-Based Access Control in SAS Programming: Enhancing Security and Authorization. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 31-42. https://upright.pub/index.php/ijrstp/article/view/133

Sachani, D. K., & Vennapusa, S. C. R. (2017). Destination Marketing Strategies: Promoting Southeast Asia as a Premier Tourism Hub. ABC Journal of Advanced Research, 6(2), 127-138. https://doi.org/10.18034/abcjar.v6i2.746

Shajahan, M. A., Richardson, N., Dhameliya, N., Patel, B., Anumandla, S. K. R., & Yarlagadda, V. K. (2019). AUTOSAR Classic vs. AUTOSAR Adaptive: A Comparative Analysis in Stack Development. Engineering International, 7(2), 161–178. https://doi.org/10.18034/ei.v7i2.711

Suresh, M., Patri, R. (2017). Agility Assessment Using Fuzzy Logic Approach: A Case of Healthcare Dispensary. BMC Health Services Research, 17. https://doi.org/10.1186/s12913-017-2332-y

Torres, A. M., Mateo, J., García, M. A., Santos, J. L. (2015). Cancellation of Powerline Interference from Biomedical Signals Using an Improved Affine Projection Algorithm. Circuits, Systems, and Signal Processing: CSSP, 34(4), 1249-1264. https://doi.org/10.1007/s00034-014-9890-6

Vennapusa, S. C. R., Fadziso, T., Sachani, D. K., Yarlagadda, V. K., & Anumandla, S. K. R. (2018). Cryptocurrency-Based Loyalty Programs for Enhanced Customer Engagement. Technology & Management Review, 3, 46-62. https://upright.pub/index.php/tmr/article/view/137

Yarlagadda, V. K., & Pydipalli, R. (2018). Secure Programming with SAS: Mitigating Risks and Protecting Data Integrity. Engineering International, 6(2), 211–222. https://doi.org/10.18034/ei.v6i2.709

Yarlagadda, V. K., Maddula, S. S., Sachani, D. K., Mullangi, K., Anumandla, S. K. R., & Patel, B. (2020). Unlocking Business Insights with XBRL: Leveraging Digital Tools for Financial Transparency and Efficiency. Asian Accounting and Auditing Advancement, 11(1), 101–116. https://4ajournal.com/article/view/94

Ying, D., Kothapalli, K. R. V., Mohammed, M. A., Mohammed, R., & Pasam, P. (2018). Building Secure and Scalable Applications on Azure Cloud: Design Principles and Architectures. Technology & Management Review, 3, 63-76. https://upright.pub/index.php/tmr/article/view/149

Downloads

Published

2021-12-31

Issue

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

Section

Peer-reviewed Article

License

Copyright (c) 2021 Malaysian Journal of Medical and Biological Research

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The CC BY-NC 4.0 license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge & be non-commercial, they don’t have to license their derivative works on the same terms.
|| View License Deed ||
|| View Legal Code ||
 
Publishing paper with the "Malaysian Journal of Medical and Biological Research" means that the author or authors retain the copyright in the paper.
This journal is granted an exclusive noncommercial reuse license by the author(s), but the author(s) are able to put the paper onto a website, distribute it to colleagues, give it to students, use it in your thesis, etc, so long as the use is not directed at a commercial advantage or toward private monetary gain.
The author(s) can reuse the figures and tables and other information contained in their paper published by Malaysian Journal of Medical and Biological Research in future papers or work without having to ask anyone for permission, provided that the figures, tables or other information that is included in the new paper or work properly references the published paper as the source of the figures, tables or other information, and the new paper or work is not direct at a private monetary gain or commercial advantage.

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