Evolutionary bioinformatics with veiled biological database for health care operations

Area of Science:

• Bioinformatics
• Health Informatics
• Computational Biology

Background:

• The proliferation of biological data processing systems in healthcare necessitates real-time information access.
• Bioinformatics integration into wireless technologies is crucial for capturing physical characteristics.
• Existing systems face challenges in processing large volumes of healthcare data efficiently.

Purpose of the Study:

• To propose an evolutionary bioinformatics model for medical sensor applications in healthcare.
• To optimize data processing using a hybrid genetic and ant colony optimization (GACO) approach.
• To minimize errors and maximize the quality of service (QoS) in real-time big data processing.

Main Methods:

• Implementation of a novel design with dedicated transmitting and receiving modules.
• Framing individual bits for enhanced bioinformatics data processing.
• Combining genetic algorithms (GA) and ant colony optimization (ACO) for system optimization (GACO).

Main Results:

• The proposed design effectively minimizes errors in big data processing stages.
• All channels are accessed according to framed bits, reducing overall data processing errors.
• Maximized quality of service (QoS) achieved by maintaining high-quality bits for bioinformatics data channels.

Conclusions:

• The developed technique successfully handles bioinformatics data for healthcare applications in real-time.
• The GACO optimization method demonstrates significant improvements in error reduction and QoS.
• Achieved a service quality of 95% in experimental evaluations, validating the model's effectiveness.