Evaluating and predicting social behavior of arsenic affected communities: Towards developing arsenic resilient society

TY - JOUR

T1 - Evaluating and predicting social behavior of arsenic affected communities

T2 - Towards developing arsenic resilient society

AU - Singh, Sushant K.

AU - Taylor, Robert W.

AU - Thadaboina, Venkatamallu

N1 - Publisher Copyright: © 2021 The Authors

PY - 2022/1

Y1 - 2022/1

N2 - This study uses six machine learning (ML) algorithms to evaluate and predict individuals' social resilience towards arsenicosis-affected people in an arsenic-risk society of rural India. Over 50% of the surveyed communities were found to be resilient towards arsenicosis patients. Logistic regression with inbuilt cross-validation (LRCV) model scored the highest accuracy (76%), followed by Gaussian distribution-based naïve Bayes (GNB) model (74%), C-Support Vector (SVC) (74%), K-neighbors (Kn) (73%), Random Forest (RF) (72%), and Decision Tree (DT) (67%). The LRCV also scored the highest kappa value of 0.52, followed by GNB (0.48), SVC (0.48), Kn (0.46), RF (0.42), and DT (0.31). Caste, education, occupation, housing status, sanitation behaviors, trust in others, non-profit and private organizations, social capital, and awareness played a key role in shaping social resilience towards arsenicosis patients. The authors opine that LRCV and GNB could be promising methods to develop models on similar data generated from a risk society.

AB - This study uses six machine learning (ML) algorithms to evaluate and predict individuals' social resilience towards arsenicosis-affected people in an arsenic-risk society of rural India. Over 50% of the surveyed communities were found to be resilient towards arsenicosis patients. Logistic regression with inbuilt cross-validation (LRCV) model scored the highest accuracy (76%), followed by Gaussian distribution-based naïve Bayes (GNB) model (74%), C-Support Vector (SVC) (74%), K-neighbors (Kn) (73%), Random Forest (RF) (72%), and Decision Tree (DT) (67%). The LRCV also scored the highest kappa value of 0.52, followed by GNB (0.48), SVC (0.48), Kn (0.46), RF (0.42), and DT (0.31). Caste, education, occupation, housing status, sanitation behaviors, trust in others, non-profit and private organizations, social capital, and awareness played a key role in shaping social resilience towards arsenicosis patients. The authors opine that LRCV and GNB could be promising methods to develop models on similar data generated from a risk society.

KW - Arsenic

KW - India

KW - Logistic regression

KW - Psychological

KW - Resilience

KW - Society

KW - Socioeconomic

UR - http://www.scopus.com/inward/record.url?scp=85121097043&partnerID=8YFLogxK

U2 - 10.1016/j.emcon.2021.12.001

DO - 10.1016/j.emcon.2021.12.001

M3 - Article

SN - 2405-6650

VL - 8

SP - 1

EP - 8

JO - Emerging Contaminants

JF - Emerging Contaminants

ER -