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Predictive Analysis on Behavioural Risk Factor Surveillance System through Ensemble Classifiers and Clusters.

K R Sekar1, Mahadev Easwar1, R Manikandan1

  • 1School of Computing, SASTRA Deemed University, India.

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Summary
This summary is machine-generated.

This study predicts health risks using the Behavioural Risk Factor Surveillance System (BRFSS) dataset. Predictive analysis achieved 92.87% accuracy in identifying necessary healthcare interventions for preventing behavioral risk factors.

Keywords:
Behavioural risk factor surveillance systemCentre for disease control and preventionClustersEnsemble classificationImmune systemWorld health organization

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Area of Science:

  • Public Health
  • Data Science
  • Preventive Medicine

Background:

  • The World Health Organization emphasizes global healthcare importance.
  • The US health department aims to enhance population immunity via the Behavioural Risk Factor Surveillance System (BRFSS).
  • Behavioral risk factors like obesity and poor nutrition impact public health.

Purpose of the Study:

  • To identify early-stage interventions for preventing behavioral risk factors.
  • To utilize predictive analysis on the BRFSS dataset for health risk assessment.
  • To guide healthcare prescriptions based on identified behavioral patterns.

Main Methods:

  • Ensemble classification and clustering techniques were applied.
  • The dataset underwent pre and post-weighted classification.
  • Healthcare needs were classified for individuals with specific behaviors (obesity, nutrition, physical activity).

Main Results:

  • Predictive analysis demonstrated high accuracy in identifying health risks.
  • The study achieved a 92.87% accuracy rate.
  • Classifications informed healthcare prescriptions for at-risk individuals.

Conclusions:

  • The analysis contributes to improving citizen health quality.
  • Early identification and intervention of behavioral risk factors are crucial.
  • Data-driven insights from BRFSS can optimize public health strategies.