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This study introduces an improved survival regression technique using partial least squares spline modeling to identify infant mortality risk factors. The method enhances public health surveillance by accurately analyzing complex datasets.

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Area of Science:

  • Public Health
  • Biostatistics
  • Survival Analysis

Background:

  • Factor discovery in public health surveillance data is challenging.
  • Multicollinearity complicates traditional regression models.
  • Identifying infant mortality risk factors is critical for public health.

Purpose of the Study:

  • To introduce an improved survival regression technique for factor discovery in public health surveillance data.
  • To address multicollinearity using a partial least squares spline modeling approach.
  • To assess significant risk factors for infant mortality using real-world data.

Main Methods:

  • Proposed a partial least squares spline modeling approach.
  • Compared the proposed method with the benchmark partial least squares Cox regression model.
  • Utilized the Akaike information criterion for accuracy assessment.
  • Applied the optimal model to infant mortality data from the Pakistan Demographic and Health Survey.

Main Results:

  • The partial least squares spline model demonstrated effectiveness in factor discovery.
  • Identified significant risk factors associated with infant mortality.
  • The recommended features provide key insights into infant survival.

Conclusions:

  • The proposed partial least squares spline modeling approach is a valuable tool for public health surveillance.
  • This method can improve the accuracy of factor discovery in the presence of multicollinearity.
  • Findings contribute to research on infant mortality and public health strategies.