Secure Medical Diagnostics via Digital DNA Sequencing and Machine Learning

Authors

  • Sushalini Ravi Student, Department of Computer Science and Engineering, Global Institute of Engineering and Technology, Ranipet, India
  • Daphney Joann Assistant Professor, Department of Artificial Intelligence & Data Science, Global Institute of Engineering & Technology, Ranipet, India
  • Kayalvizhi Loganathan Student, Department of Computer Science and Engineering, Global Institute of Engineering and Technology, Ranipet, India
  • Harinie Sekar Student, Department of Computer Science and Engineering, Global Institute of Engineering and Technology, Ranipet, India

Abstract

This paper presents a cutting-edge web-based healthcare management system that offers safe and effective healthcare services while bridging the gap between hospitals, physicians, and patients. Hospitals can use the platform to manage physicians, register patients, and set service fees. Convenient hospital and doctor selection, safe payment processing, and a personalized token that allows encrypted contact with the selected physician are all available to patients. Patients and physicians may communicate easily thanks to the system's encrypted chat interface. Doctors use the Smith-Waterman method to examine DNA sequences uploaded by patients, producing accurate diagnosis results. The platform is managed by an administrator, who guarantees efficient operations and strong security compliance. The system uses the AES encryption method to safeguard private patient data, guaranteeing data integrity and confidentiality throughout transmission and storage. The technology provides a novel and individualized approach to contemporary healthcare by fusing secure communication channels with cutting-edge DNA analysis capabilities, promoting efficiency, security, and confidence in medical services.

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Published

11-05-2025

Issue

Section

Articles

How to Cite

[1]
S. Ravi, D. Joann, K. Loganathan, and H. Sekar, “Secure Medical Diagnostics via Digital DNA Sequencing and Machine Learning”, IJRESM, vol. 8, no. 5, pp. 56–59, May 2025, Accessed: May 18, 2025. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/3260