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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Explaining Predictive Model Performance: An Experimental Study of Data Preparation and Model Choice.

Hamidreza Ahady Dolatsara1, Ying-Ju Chen2, Robert D Leonard3

  • 1Graduate School of Management, Clark University, Worcester, Massachusetts, USA.

Big Data
|October 6, 2021
PubMed
Summary
This summary is machine-generated.

Current predictive modeling practices often lead to poor performance due to isolated data preparation decisions. Rigorous application of the scientific method is needed for improved predictive accuracy and reproducibility in research.

Keywords:
United Network for Organ Sharing (UNOS)artificial intelligencedata miningdata sciencedesign of experimentsscientific method

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Area of Science:

  • Applied predictive modeling
  • Data science methodology
  • Reproducibility in research

Background:

  • Confirmatory modeling has historically dominated applied research.
  • Predictive modeling of large datasets is increasingly important across various scientific fields.
  • Current heuristic-based frameworks for predictive modeling involve isolated decisions that can compromise performance.

Purpose of the Study:

  • To experimentally evaluate the impact of data preparation and model selection factors on predictive accuracy.
  • To investigate interactions between early and later decisions in the predictive modeling pipeline.
  • To highlight the need for enhanced rigor and a standardized framework in applied predictive research.

Main Methods:

  • A factorial experimental design was employed.
  • Six factors (numerical imputation, categorical imputation, encoding, subsampling, feature selection, machine learning algorithm) and their interactions were evaluated.
  • 10,800 models were assessed on a large, public heart transplantation dataset using 5 independent test partitions.

Main Results:

  • Decisions made during data preparation and model selection significantly impact predictive accuracy.
  • Interactions between early and later modeling decisions were confirmed to affect outcomes.
  • Current independent decision-making practices can negatively influence predictive performance.

Conclusions:

  • The study underscores the necessity for improved rigor in applied predictive research.
  • A scientific method-informed framework is proposed to enhance predictive modeling.
  • Establishing standards for reproducibility in predictive research is crucial for reliable scientific advancement.