Handling High-Dimensional Healthcare Data

Abstract

This chapter explores techniques and optimization strategies for managing high-dimensional data in healthcare, including genomics, medical imaging, and multi-omics, to enhance machine learning model accuracy and efficiency. It addresses challenges such as the “curse of dimensionality,” data sparsity, and computational inefficiencies by introducing preprocessing methods, dimensionality reduction techniques like PCA and autoencoders, and optimization strategies such as genetic algorithms and gradient descent. Applications in genomics and radiomics demonstrate the transformative potential of these methods in disease prediction and treatment personalization. Additionally, the chapter highlights ethical and privacy considerations essential for managing sensitive healthcare data, ensuring compliance with regulations while advancing data-driven medicine. This comprehensive examination of high-dimensional data management bridges the gap between technical solutions and practical applications, contributing to improved patient outcomes and innovative healthcare solutions.

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