Cover Image
QR Code

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

Background: Each year, millions of children under five die globally, with many of these deaths being preventable. The situation is particularly concerning in low sociodemographic index (LSDI) states of India, where the under-five mortality rate is 45 children per 1000 live births. This study aimed t...

Full description

Published in:Journal of Research in Health Sciences Vol. 25; no. 3; pp. 1 - 10
Main Authors: Vishwakarma, Mukesh, Tyagi, Gargi, Radhakrishnan, Rehana Vanaja
Format: pictorial research tables/charts Journal Article
Published: Hamadan University of Medical Sciences, School of Public Health Summer2025
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187102583&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 187102583
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        22287795
        903Q
      jtl: Journal of Research in Health Sciences
      issn: 22287795
      maglogo: N
    pubinfo:
      dt: Summer2025
      vid: 25
      iid: 3
      pid: 54266
      pub: Hamadan University of Medical Sciences, School of Public Health
    artinfo:
      ui:
        187102583
        187102583
        187102583
        10.34172/jrhs.9033
        187102583
      ppf: 1
      ppct: 9
      formats:
        fmt:
          @attributes:
            type: P
      tig:
        atl: Survival Machine-Learning Approach for Predicting Under-Five Mortality in Low Sociodemographic Index States of India.
      aug:
        au:
          Vishwakarma, Mukesh
          Tyagi, Gargi
          Radhakrishnan, Rehana Vanaja
        affil: Department of Mathematics and Statistics, Faculty of Mathematics and Computing, Banasthali Vidyapith, Rajasthan, India
      sug:
        subj:
          Infant Mortality Risk Factors
          Child Mortality Risk Factors
          Risk Assessment
          Prediction Models Evaluation
          Machine Learning Algorithms Evaluation
          Sociodemographic Factors
          Geographic Factors
          Human
          India
          Female
          Adolescence
          Adult
          Cross Sectional Studies
          Survival Analysis
          Cox Proportional Hazards Model
          ROC Curve
          Descriptive Statistics
          Confidence Intervals
          Mothers Psychosocial Factors
          Maternal Age
          Educational Status
          Income
          Infant, Low Birth Weight
          Low Socioeconomic Status
          Adolescent: 13-18 years
          Adult: 19-44 years
          Female
      ab: Background: Each year, millions of children under five die globally, with many of these deaths being preventable. The situation is particularly concerning in low sociodemographic index (LSDI) states of India, where the under-five mortality rate is 45 children per 1000 live births. This study aimed to predict under-five mortality and determine related key factors. Study Design: A cross-sectional study. Methods: This study analyzed National Family Health Survey-5 (NFHS-5) data related to 94,202 children from the LSDI states of India. Several survival models were tested, including Cox proportional hazards, random survival forest, and gradient-boosted survival, to identify factors linked to child mortality. Model performance was evaluated using metrics such as the concordance index, integrated Brier score, and time-dependent receiver operating characteristic (ROC) curves. Results: Among the studied children, 4.5% (4,284) died before their fifth birthday. The risk of death was higher in children born to younger (15-25 years) mothers (hazard ratio [HR] = 1.113, 95% confidence interval (CI): 1.034, 1.198; P < 0.001), uneducated mothers (HR = 1.263, 95% CI: 1.098-1.454; P < 0.0001), mothers with a poorer wealth index (HR = 1.719, 95% CI: 1.475-2.003; P < 0.0001), and children with low birth weight (HR = 2.091, 95% CI: 1.934-2.26; P < 0.001). The random survival forest model outperformed in identifying these risk factors. Conclusion: This study highlights the importance of empowering women through education, improving family planning, addressing poverty, and providing equitable healthcare to reduce child mortality. These insights can help shape policies and initiatives to improve the survival and health of children in vulnerable communities.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N