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Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk.

Jean Gaudart1, Belco Poudiougou, Stéphane Ranque

  • 1Medical Statistics and Informatics Research Team, LIF-UMR 6166, CNRS/Aix-Marseille University, Faculty of Medicine, 27 Bd Jean Moulin 13385 Marseille Cedex 05, France. jean.gaudart@medecine.univ-mrs.fr

BMC Medical Research Methodology
|July 20, 2005
PubMed
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This study introduces an Oblique Decision Tree (ODT) model for disease cluster detection, improving upon traditional methods by not requiring pre-specified cluster shapes or sizes. The ODT model effectively identified high-risk malaria clusters in West Africa, demonstrating its utility in epidemiological surveillance.

Area of Science:

  • Epidemiology
  • Spatial Analysis
  • Biostatistics

Background:

  • Classical disease cluster detection methods rely on predefined circular scan windows, which can influence results.
  • The shape, size, and center of scanning windows are critical and can lead to variable outcomes.
  • Identifying disease clusters without a known source requires flexible spatial analysis techniques.

Purpose of the Study:

  • To develop and evaluate an Oblique Decision Tree (ODT) algorithm for identifying disease clusters without pre-specifying cluster parameters.
  • To compare the ODT model's performance against a standard spatial scan statistic (SaTScan) for disease cluster detection.

Main Methods:

  • Developed an optimal Oblique Decision Tree (ODT) algorithm in R2 for oblique spatial partitioning.

Related Experiment Videos

  • Applied the ODT model to malaria incidence data from a West African village during the dry season.
  • Utilized Monte-Carlo inference for statistical testing of spatial patterns and compared results with Kulldorff's SaTScan.

    • Main Results:

    • The ODT procedure identified four risk classes, with the highest class showing 60% infection rate in children (CI95% [52.22-67.55]).
    • Monte-Carlo inference confirmed the statistical significance of the ODT-identified spatial pattern (p < 0.0001).
    • SaTScan identified a significant high-risk cluster (54.21% infection rate, CI95% [47.51-60.75]) located within the ODT's primary high-risk area, near a mosquito breeding site.

    Conclusions:

    • Oblique Decision Tree models offer an advancement over classical scanning procedures for disease cluster detection.
    • ODT models detect potential disease clusters independent of assumptions about cluster shape, size, or center.
    • The ODT approach provides a robust method for spatial epidemiological analysis and public health surveillance.