Brain Informatics Technology

8Machine Learning Techniques for Brain and Health Data

Shubhra Dixit¹, Surbhi Gupta² and Ajay Sharma^3*

¹Department of Electronics & Communication Engineering, ASET, Amity University Uttar Pradesh, Noida, India

²Department of Mathematics, AIAS, Amity University Uttar Pradesh, Noida, India

³Department of Mechanical Engineering, ASET, Amity University Uttar Pradesh, Noida, India

Abstract

This paper discusses the transformative possibility of machine learning in analyzing health and brain data. We discuss supervised learning techniques for regression models and classification models, with a focus on applications in disease diagnosis and progression prediction. We also discuss the roles of unsupervised learning such as dimensionality reduction, clustering, and hidden pattern discovery in data.

With the challenges associated with limited labeled data, including EHRs and rare diseases, semi-supervised learning becomes an attractive technique. We also talk about how reinforcement learning can be used for applications in assistive technology in BCIs and, further, to tailor the personal treatment plan to an optimum level.

Deep learning, especially in the form of RNNs and CNNs, is also used to process complex medical images and time-series data. NLP methods are explored to haul out valuable information from unstructured medical texts. The concluding section examines the practical implications of these methods in predictive analytics, genomics, neuroimaging, and remote health monitoring. ...