New research aims to leverage data to overcome cultural taboos around addiction in developing countries.
New research has shown how machine learning can help address the stigma of substance abuse, particularly in developing countries, where it is usually difficult to get treatment.
Experts from the University of Waterloo in Canada have harnessed machine learning and anonymised data to gain a clearer picture of the underlying factors that influence tendencies to abuse alcohol and drugs.
The research paper, titled “A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors”, was published in the journal Annals of Data Science, by Enamul Haque and co-authors Uwaise Ibna Islam, Dheyaaldin Alsalman, Muhammad Nazrul Islam, Mohammad Ali Moni, and Iqbal H Sarker.
The approach provides rare insight into a topic often neglected due to social and cultural taboos. The research team hopes that their work can eventually make it easier for people to get help.
Several significant risk factors were identified, such as family relationships, a curiosity to experiment with drugs, and relationships with friends who also suffer from substance abuse.
“In a country like Bangladesh, people can be hesitant to discuss substance abuse issues,” said the lead author of the research Enamul Haque, a Ph.D. researcher in computer science at the University of Waterloo.
“This kind of research will enable policy-makers to have better information and then be able to design better programs to help address substance abuse.”
The new research incorporated data from multiple sources, including mass online surveys and one-on-one interviews. Most of the survey data were drawn from developing countries in South Asia.
“Within the countries where we conducted the survey, we collected data from a broad and diverse pool of respondents,” Haque said. “We looked for different respondents based on age, gender and socio-economic context.”
The team first collected a massive amount of data to be used in the study. They then relied on machine-learning algorithms to identify patterns and key risk factors for substance abuse. In order to carry out the computer science part of the research, the team set up multiple stages of data analysis and refinement.
“I really hope this research can help people dealing with substance abuse issues and get them the support they need,” Haque said.