Brain Informatics Technology

10Machine Learning in Brain and Health Data: Current Advances and Future Pathways

Selvani Deepthi Kavila^1*, Rajesh Bandaru², Moni Sushma Deep Kavila¹ and K. Veera Raghavendra Rao¹

¹Department of CSE (Artificial Intelligence and Machine Learning), Anil Neerukonda Institute of Technology and Sciences(A), Visakhapatnam, India

²Department of CSE, GST, GITAM (Deemed to be University), Visakhapatnam, India

Abstract

Machine learning (ML) is revolutionizing how we understand and treat brain and health issues. Historically, researchers primarily used basic statistical methods to analyze brain images and electrical activity. These methods laid the foundation for early insights into brain function and pathology. However, with the advent of advanced ML techniques like supervised learning, unsupervised learning, and deep learning, the landscape has dramatically changed. Today, machine learning enables healthcare professionals to investigate deeper into complex datasets derived from brain scans, genetic information, and electronic health records. By using these techniques, doctors can make more precise diagnoses, develop suited treatment plans, and forecast health outcomes with exceptional accuracy. Machine learning algorithms can examine through vast amounts of data to identify fine patterns that correlate with specific neurological disorders or predict responses to different treatment options. Researchers anticipate that machine learning will enable real-time analysis of brain data, facilitate ...