Advances in technology have progressed methods of automated learning available to the biomedical engineering field.
In the textbook “Computational Learning Approaches to Data Analytics in Biomedical Applications,” Dr. Obafemi-Ajayi and colleagues explore theoretical and practical aspects of computational learning.
About her research
Specific factors of biomedical potential fuel interest in data-driven solutions:
- Expanding personalized medicine
- Improving means of managing the aging population
- Furthering hope for curing stubborn health problems
The explosion of data currently available roots in modern technological tools.
“What sets fire to this fuel are the new advances in computational intelligence capabilities, together with improvements in the computing hardware they depend on,” Obafemi-Ajayi said.
Discoveries from applications of computational learning methods allow clinicians to address questions related to diseases like Alzheimer’s, Parkinson’s and Autism Spectrum Disorders among other conditions.
Using such advances in technological tools, Obafemi-Ajayi and her colleagues were able to conduct a thorough analysis of the diverse methods of computational learning currently available in their research community.
They presented their findings in an in-depth literature review.
“We hope this text will provide researchers with a launching pad to explore the use of machine learning and statistics-related methods for their own projects.”
The results of a conference tutorial led by Obafemi-Ajayi and her colleagues served as the genesis of their textbook.