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Optimizing Dementia Diagnosis Through Distance-Correlation Feature Space and Dimensionality Reduction.

Pablo Zubasti1, Miguel A Patricio1, Antonio Berlanga1

  • 1Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Colmenarejo 28270, Spain.

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|June 13, 2025
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Summary
This summary is machine-generated.

This study introduces a novel dimensionality reduction algorithm using distance-correlation feature spaces and graph embeddings to optimize machine learning models for faster, more scalable AI systems. The method enhances dementia diagnosis model performance.

Keywords:
Dimensionality reductiondementia diagnosisdistance-correlationgraph embeddings

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Dimensionality reduction is crucial for simplifying machine learning models, improving performance, and reducing execution time.
  • Common methods include feature selection and feature extraction.
  • Optimizing AI systems requires efficient model simplification.

Purpose of the Study:

  • To propose a novel dimensionality reduction algorithm.
  • To apply this algorithm in the context of dementia diagnosis using learning models.
  • To enhance the optimization of the diagnostic process.

Main Methods:

  • A distance-correlation feature space is proposed.
  • A dimensionality reduction algorithm is defined based on space transformations and graph embeddings.
  • The methodology is applied to learning models for dementia diagnosis.

Main Results:

  • The proposed algorithm facilitates rapid result generation and enhances system scalability.
  • The approach optimizes learning models for improved performance in diagnostic tasks.
  • Demonstrates effective dimensionality reduction for complex AI problems.

Conclusions:

  • The novel distance-correlation based dimensionality reduction technique offers significant advantages.
  • This method effectively optimizes machine learning models for applications like dementia diagnosis.
  • The approach contributes to more efficient and scalable artificial intelligence solutions.