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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Identifying population segments for effective intervention design and targeting using unsupervised machine learning:

Elisabeth Engl1, Peter Smittenaar1, Sema K Sgaier1,2,3

  • 1Surgo Foundation, Washington, DC, 20011, USA.

Gates Open Research
|November 12, 2019
PubMed
Summary

Targeted interventions improve health by understanding individual behavior drivers. Psycho-behavioral segmentation offers a scalable method for designing effective, personalized health programs globally.

Keywords:
Psycho-behavioral segmentationbehavioral sciencecluster analysisglobal health and development; targeted intervention designhuman heterogeneitymachine learningunsupervised learning

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Area of Science:

  • Behavioral Science
  • Public Health
  • Data Science

Background:

  • One-size-fits-all behavior change interventions are ineffective.
  • Psycho-behavioral segmentation can tailor interventions but is rarely used at scale.
  • Lack of guidance hinders program designers and data scientists in implementing segmentation.

Purpose of the Study:

  • To provide an end-to-end guide for implementing psycho-behavioral segmentation.
  • To illustrate critical choices and steps in segmentation design and analysis.
  • To demonstrate the application of segmentation in global development, using a health intervention case study.

Main Methods:

  • Conceptualization and selection of segmentation dimensions.
  • Design of qualitative and quantitative primary research.
  • Algorithm selection, analysis, and subjective evaluation of outputs.
  • Prioritization of segments and matching interventions via appropriate channels.

Main Results:

  • A comprehensive framework for psycho-behavioral segmentation is presented.
  • The process involves planning, research design, analysis, and implementation.
  • A case study on voluntary medical male circumcision demonstrates the method's utility.

Conclusions:

  • Psycho-behavioral segmentation enables targeted interventions by understanding behavioral heterogeneity.
  • The provided framework can guide scalable implementation in global development.
  • The principles are applicable across various contexts where behavior change is crucial.