Prediction of suicidal ideation among Chinese college students based on radial basis function neural network

Abstract

BackgroundSuicide is one of the leading causes of death for college students. The predictors of suicidal ideation among college students are inconsistent and few studies have systematically investigated psychological symptoms of college students to predict suicide. Therefore, this study aims to develop a suicidal ideation prediction model and explore important predictors of suicidal ideation among college students in China.MethodsWe recruited 1,500 college students of Sichuan University and followed up for 4 years. Demographic information, behavioral and psychological information of the participants were collected using computer-based questionnaires. The Radial Basis Function Neural Network (RBFNN) method was used to develop three suicidal ideation risk prediction models and to identify important predictive factors for suicidal ideation among college students.ResultsThe incidence of suicidal ideation among college students in the last 12 months ranged from 3.00 to 4.07%. The prediction accuracies of all the three models were over 91.7%. The area under curve scores were up to 0.96. Previous suicidal ideation and poor subjective sleep quality were the most robust predictors. Poor self-rated mental health has also been identified to be an important predictor. Paranoid symptom, internet addiction, poor self-rated physical health, poor self-rated overall health, emotional abuse, low average annual household income per person and heavy study pressure were potential predictors for suicidal ideation.ConclusionsThe study suggested that the RBFNN method was accurate in predicting suicidal ideation. And students who have ever had previous suicidal ideation and poor sleep quality should be paid consistent attention to.

Frequency and machine learning predictors of depressive, anxiety, obsessive-compulsive symptoms, and suicidal ideation among university students

“…Noteworthily, suicidal ideation at baseline could be leveraged by the machine to predict suicidal ideation after six months, but it did not yield a strictly statistically significant role. To our knowledge, there have been several studies implementing machine learning models leveraging previous suicidal ideation to predict future suicidal ideation (Benjet et al, 2022;Liao et al, 2022;Malone et al, 2021), although timeframes from baseline to follow-up did not coincide with our six-month timeframe. Substantially, previous suicidal ideation is an important is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint…”

“…Noteworthily, suicidal ideation at baseline could be leveraged by the machine to predict suicidal ideation after six months, but it did not yield a strictly statistically significant role. To our knowledge, there have been several studies implementing machine learning models leveraging previous suicidal ideation to predict future suicidal ideation (Benjet et al, 2022; Liao et al, 2022; Malone et al, 2021), although timeframes from baseline to follow-up did not coincide with our six-month timeframe. Substantially, previous suicidal ideation is an important factor, posing a greater risk of future suicidal ideation and suicidal behavior (Bafna et al, 2022), but in our analysis did not reach canonical statistical significance (although it should be taken into account that the machine learning model would lose a great proportion of its accuracy without data from previous suicidal ideation – Figure 3A).…”

Frequency and machine learning predictors of severe depressive symptoms and suicidal ideation among university students

“…, 2022 ; Liao et al. , 2022 ; Malone et al. , 2021 ), although time frames from baseline to follow-up did not coincide with our 6-month time frame.…”

“…Noteworthily, suicidal ideation at baseline could be leveraged by the machine to predict suicidal ideation after 6 months, but it did not yield a strictly statistically significant role. To our knowledge, there have been several studies implementing machine learning models leveraging previous suicidal ideation to predict future suicidal ideation (Benjet et al, 2022;Liao et al, 2022;Malone et al, 2021), although time frames from baseline to follow-up did not coincide with our 6-month time frame. Substantially, previous suicidal ideation is an important factor, posing a greater risk of future suicidal ideation and suicidal behaviour (Bafna et al, 2022), but in our analysis, this prediction did not reach canonical statistical significance (although it should be taken into account that the machine learning model would lose a great proportion of its accuracy without data from previous suicidal ideation -Figure 3A).…”

Insomnia moderates the association between psychotic-like experiences and suicidal ideation in a non-clinical population: a network analysis

“…Our findings extend these observations over the non-clinical sample of individuals with less severe psychopathological symptoms. Moreover, a recent study performed in college students demonstrated that previous suicidal ideation and low subjective quality of sleep are the most robust predictors of the current suicidal ideation [ 51 ]. Other factors associated with higher risk of the current suicidal ideation in this sample included paranoid thoughts, internet addiction, poor self-rated physical health, poor self-rated overall health, emotional abuse, low average annual household income per person and heavy study pressure.…”