A0454
Title: Correlates of suicide ideation among young adults: Insights from machine learning algorithms
Authors: Mogana Darshini Ganggayah - Monash University Malaysia (Malaysia) [presenting]
Erniel Barrios - Monash University Malaysia (Malaysia)
Hariharan Muniandy - EY (Malaysia)
Abstract: Target 3.4 (including suicide mortality rate as one indicator) of sustainable development goals set to reduce premature death from noncommunicable diseases by one-third in 2030. In 2019, global suicide rates were estimated at 9.2 per 100,000 population; this is highest in Europe at 12.8 and also relatively higher in Southeast Asia at 10.1. Among adolescents (15-24 years old), the suicide rate is 7.97 and exceeds over 20 in many countries. Suicide was the fourth leading cause of death among 15-29 years old in 2016. Many suicides happen impulsively in moments of crisis, which is triggered by poor mental health. Some machine learning algorithms are used to identify possible risk factors associated with suicide ideation among Filipino youths based on the 5th Young Adult Fertility and Sexuality Survey (YAFS5). Some indicators related to personal experience, internet usage (especially heavy engagement in social media), intake of alcohol, marital status, experience of sexual harassment, and sleep difficulties lead to distress associated with mental health conditions that are associated with suicidal ideation. This underscores the necessity for a holistic approach to suicide prevention that addresses a wide spectrum of risk factors. The significant role of statistical machine learning is also exhibited in further extracting insights from survey data.
