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Different Routes or Methods of Application for Dimensionality Reduction in Multicenter Studies Databases

Author

Listed:
  • Nisa Boukichou-Abdelkader

    (School of Doctorate in Sciences, Technologies and Engineering, University of Granada, 18012 Granada, Spain
    Data Science Unit, Health Innovation of La Rioja, Rioja Health Foundation, CIBIR, 26006 Logroño, Spain)

  • Miguel Ángel Montero-Alonso

    (Department of Statistic and Operational Research, University of Granada, 18016 Granada, Spain)

  • Alberto Muñoz-García

    (Department of Statistic, University Carlos III of Madrid, 28903 Madrid, Spain)

Abstract

Technological progress and digital transformation, which began with Big Data and Artificial Intelligence (AI), are currently transforming ways of working in all fields, to support decision-making, particularly in multicenter research. This study analyzed a sample of 5178 hospital patients, suffering from exacerbation of chronic obstructive pulmonary disease (eCOPD). Because of differences in disease stages and progression, the clinical pathologies and characteristics of the patients were extremely diverse. Our objective was thus to reduce dimensionality by projecting the data onto a lower dimensional subspace. The results obtained show that principal component analysis (PCA) is the most effective linear technique for dimensionality reduction. Four patient profile groups are generated with similar affinity and characteristics. In conclusion, dimensionality reduction is found to be an effective technique that permits the visualization of early indications of clinical patterns with similar characteristics. This is valuable since the development of other pathologies (chronic diseases) over any given time period influences clinical parameters. If healthcare professionals can have access to such information beforehand, this can significantly improve the quality of patient care, since this type of study is based on a multitude of data-variables that can be used to evaluate and monitor the clinical status of the patient.

Suggested Citation

  • Nisa Boukichou-Abdelkader & Miguel Ángel Montero-Alonso & Alberto Muñoz-García, 2022. "Different Routes or Methods of Application for Dimensionality Reduction in Multicenter Studies Databases," Mathematics, MDPI, vol. 10(5), pages 1-14, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:696-:d:756649
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    References listed on IDEAS

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    Keywords

    PCA; MICE; RF&IV; simulation; eCOPD;
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