Researchers at Canada's University of Waterloo used machine learning and anonymized data to better understand drug and alcohol abuse in developing countries, where the stigma of substance abuse can make it difficult to get help.
Using data from mass online surveys, one-on-one interviews, and other sources, mostly from South Asia, the researchers employed machine-learning algorithms to identify patterns and key risk factors for substance abuse.
They found that underlying factors included family relationships, general curiosity about drug experimentation, and friends who abuse substances.
Said Waterloo's Enamul Haque, "This kind of research will enable policymakers to have better information and then be able to design better programs to help address substance abuse."
From University of Waterloo News (Canada)
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