New York, July 16: Machine Learning (ML) may help identify handgun purchasers who are at high risk of suicide as it identifies individual and community characteristics that are predictive of firearm suicide, says a new study.
The study, published in the journal JAMA Network Open, suggests risk factors identified by the algorithm predictive of firearm suicide included older age, first-time firearm purchaser, white race, living near the gun dealer, and purchasing a revolver.
“While limiting access to firearms among individuals at increased risk for suicide presents a critical opportunity to save lives, accurately identifying those at risk remains a key challenge. Our results suggest the potential utility of handgun records in identifying high-risk individuals to aid suicide prevention,” said lead author Hannah S. Laqueur from the University of California – Davis Health.
Machine learning (ML) may help identify handgun purchasers who are at high risk of suicide as it identifies individual and community characteristics that are predictive of firearm suicide, says a new study. pic.twitter.com/vR5qcvQREk
— IANS (@ians_india) July 16, 2022
For the study, the team analysed data from almost five million firearm transactions from the California Dealer Record of Sale database (DROS). The records, which spanned from 1996 to 2015, represented almost two million individuals.
They also examined firearm suicide data from California death records between 1996 and 2016.
The team generated 41 predictor variables from the transaction data. Among other data points, the researchers looked at handgun categories (such as a revolver or semiautomatic pistol), caliber size, price, where the gun was purchased, the buyer’s previous gun purchases, gun purchases, gender, race and ethnicity, and age.
The researchers ran a random forest classification algorithm — which can generate predictions on a wide range of data. They used the transaction-level data to predict firearm suicide within one year of purchase.
Among the top 5 per cent of transactions identified as the most risky, close to 40 per cent, or 379 of 983, were associated with a purchaser who died by firearm suicide within one year.
Among the very small number of transactions with a random forest score or predicted probability of 0.95 and above, 69 per cent, or 24 of 35, were affiliated with a purchaser who died by firearm suicide within one year.
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