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Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.

Zhaoyang Zhang1, Hua Fang2, Honggang Wang3

  • 1Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA, 01655, USA.

Journal of Medical Systems
|April 30, 2016
PubMed
Summary

We developed a new validation framework (MIV) to find the optimal number of clusters in complex eHealth trial data. This method effectively handles big, longitudinal datasets with missing values, improving unsupervised learning accuracy.

Keywords:
Big dataFuzzy clusteringLongitudinal trialMissing dataMultiple imputationValidation

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Area of Science:

  • * Health Informatics
  • * Computational Statistics
  • * Data Science

Background:

  • * eHealth services increasingly rely on web-delivered trials generating large, complex datasets.
  • * Longitudinal, high-dimensional data with missing values pose challenges for clustering analysis.
  • * Validating the optimal number of clusters in such data is a significant hurdle for unsupervised learning methods.

Purpose of the Study:

  • * To propose a novel Multiple Imputation-based Validation (MIV) framework and algorithms for clustering big, longitudinal eHealth data with missing values.
  • * To enhance the accuracy of unsupervised learning in eHealth by addressing the challenge of determining the optimal number of clusters.
  • * To provide a generalizable approach for fuzzy-logic based clustering methods applied to complex trial data.

Main Methods:

  • * Developed a Multiple Imputation-based Validation (MIV) framework building upon existing MIfuzzy clustering.
  • * Implemented MIV algorithms for auto-searching and synthesizing a suite of Multiple Imputation-based validation methods and indices.
  • * Included conventional (bootstrap, cross-validation) and emerging (modularity-based) indices, alongside the Xie and Beni index specific to fuzzy clustering.

Main Results:

  • * Demonstrated MIV framework performance on a large, real-world web-delivered trial dataset and through simulation.
  • * Identified that Multiple Imputation-based Xie and Beni index is particularly suitable for fuzzy clustering of complex, incomplete longitudinal eHealth data.
  • * Confirmed the effectiveness of the MIV approach in accurately detecting the optimal number of clusters.

Conclusions:

  • * The proposed MIV framework and algorithms offer a robust solution for clustering big, incomplete longitudinal eHealth data.
  • * The MIV concept and algorithms are adaptable to various clustering techniques processing complex trial data.
  • * This work advances unsupervised learning applications in eHealth by improving cluster validation for challenging datasets.