摘要
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 distributionbased 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.
