What is it about?
This study uses six machine learning algorithms to evaluate and predict individuals' social resilience towards arsenicosis-affected people in an arsenic-risk society of rural India. A social resilience index to arsenicosis patients or people potentially may have arsenicosis is developed and predicted.
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Why is it important?
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 logistic regression with inbuilt cross validation function and Gaussian distribution-based Naïve Bayes algorithms outperformed the rest of the models.
Read the Original
This page is a summary of: Evaluating and predicting social behavior of arsenic affected communities: Towards developing arsenic resilient society, Emerging Contaminants, January 2022, Elsevier,
DOI: 10.1016/j.emcon.2021.12.001.
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