Using machine learning to forecast domestic homicide via police data and super learning

Abstract

We explore the feasibility of using machine learning on a police dataset to forecast domestic homicides. Existing forecasting instruments based on ordinary statistical instruments focus on non-fatal revictimization, produce outputs with limited predictive validity, or both. We implement a “super learner,” a machine learning paradigm that incorporates roughly a dozen machine learning models to increase the recall and AUC of forecasting using any one model. We purposely incorporate police records only, rather than multiple data sources, to illustrate the practice utility of the super learner, as additional datasets are often unavailable due to confidentiality considerations. Using London Metropolitan Police Service data, our model outperforms all extant domestic homicide forecasting tools: the super learner detects 77.64% of homicides, with a precision score of 18.61% and a 71.04% Area Under the Curve (AUC), which, collectively and severely, are assessed as “excellent.” Implications for theory, research, and practice are discussed.

Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review

“…By analyzing large amounts of data, ML algorithms can identify complex relationships and hidden links behind phenomena that are not obvious to human observers ( 20 ). The key aspect of ML is its capability to build predictive models, demonstrated by its ability to anticipate clinical outcomes such as suicidal ideation, impulsivity, and VB ( 19 , 21 , 22 ). This attribute renders ML a promising instrument for unraveling the intricate interplay between schizophrenia and VB, thereby aiding healthcare providers in the early identification of individuals susceptible to VB ( 23 , 24 ).…”