Showing posts from September, 2018

Machine learning: Prediction suicidal behaviour based on drug abuse and mental health

There is some well-known correlation between certain mental disorders and suicidal ideation or suicidal behaviour. I was interested in whether a machine learning model could be trained to identify suicidal behaviour based on mental health, biological health, and drug abuse questions. The dataset I was working with came from the public Criminal Justice Drug Abuse Treatment Studies (CJ-DATS): The Criminal Justice Co-Occurring Disorder Screening Instrument (CJ-CODSI) and had new 353 new admissions to a prison-based substance abuse treatment program (137 Whites, 96 African Americans, and 120 Latinos). The questionnaire overall was very detailed with 789 attributes, either direct data input or compound fields, based on other fields. The variable I wanted to forecast was called SATTLF (Suicide Attempts Lifetime) and to make sure that no suicide or suicide-related questions were left in the source data, I removed 164 fields (including SATTLF), which directly or indirectly referred to