Related Experiment Video

Updated: Sep 11, 2025

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

915

Survival Machine-Learning Approach for Predicting Under-Five Mortality in Low Sociodemographic Index States of India

Mukesh Vishwakarma1, Gargi Tyagi1,2, Rehana Vanaja Radhakrishnan3

  • 1Department of Mathematics and Statistics, Faculty of Mathematics and Computing, Banasthali Vidyapith, Rajasthan, India.

Journal of Research in Health Sciences
|August 18, 2025
PubMed

Insights

Millions of children die before age five, especially in India. Key risk factors include young maternal age, lack of education, poverty, and low birth weight, highlighting the need for improved healthcare and education.

Area of Science:

  • Public Health
  • Biostatistics
  • Demography

Background:

  • Millions of preventable under-five deaths occur globally each year.
  • India's low sociodemographic index (LSDI) states face a high under-five mortality rate of 45 per 1000 live births.
  • Predicting child mortality and identifying associated factors are critical for intervention.

Purpose of the Study:

  • To predict under-five mortality in India's LSDI states.
  • To identify key demographic and socioeconomic factors associated with child mortality.

Main Methods:

  • A cross-sectional study analyzing National Family Health Survey-5 (NFHS-5) data from 94,202 children.
  • Comparison of survival models: Cox proportional hazards, random survival forest, and gradient-boosted survival.
  • Model performance evaluated using concordance index, integrated Brier score, and time-dependent ROC curves.

Main Results:

  • 4.5% of children studied died before their fifth birthday.
  • Increased mortality risk observed with younger maternal age (15-25 years), uneducated mothers, poorer wealth index, and low birth weight.
  • The random survival forest model demonstrated superior performance in identifying risk factors.

Conclusions:

  • Empowering women through education and improving family planning are crucial.
  • Addressing poverty and ensuring equitable healthcare access are vital for reducing child mortality.
  • Findings can inform policies to enhance child survival in vulnerable populations.
Abstract

Keywords:
Child mortalitiesCox modelsROC curvesRandom forestSurvival analyses

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

634

Related Concept Videos

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
260
Applications of Life Tables01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
123
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
395
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
133
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
159
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
196