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Machine learning prediction of suicide attempts in major depression: Feature selection and model development using
Jia Huang1, Lei Ding1, Yousong Su1
1Division of Mood Disorder, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China.
Background:
The study aimed to explore the risk factors of suicidal attempts (SA) in patients with major depressive disorders (MDD).
Methods:
Cross-sectional analysis of 3247 MDD patients from the National Survey on Symptomatology of Depression (NSSD) was conducted, with data split 7:3 for training/validation. Boruta's Algorithm and Lasso screened predictors, while logistic regression and nomogram were used to identify independent risk factors of SA and visualize the overall impact of these factors on the SA risk of each patient.
Results:
392 (12.1 %) patients were found to have a history of SA. Boruta's Algorithm and Lasso analysis identified 20 variables as significant risk factors of SA, especially self-harm and a sense of decreased ability. Logistic regression analysis found that No. of hospitalization (OR = 1.14, 95 %CI:1.06, 1.23), SSRIs use(OR = 1.57,95 %CI:1.03,2.38), antipsychotics use (OR = 1.82,95 %CI:1.08,3.04), mood congruent psychosis (OR = 1.32,95 %CI:1.03,1.70), self-harm (OR = 70.90,95 %CI:45.70, 113.00), gastrointestinal system complaints (OR = 1.42,95 %CI:1.13, 1.78), weight gain (OR = 2.11,95 %CI:1.04, 4.29),the feelings of helplessness(OR = 1.47,95 %CI:1.11,1.95), unhappiness (OR = 1.52,95 %CI:1.14, 2.00) and derealization (OR = 1.33,95 %CI:1.02, 1.72) were independently and significantly associated with increased risk of SA in MDD patients. The prediction model showed robust performance, with an area under the curves (AUC) of the receiver-operator characteristics of 0.935 and 0.937 in the training set and validation set, respectively.
Conclusion:
The findings may help develop assessment tools for suicide risk in MDD patients and provide clues for further mechanistic studies.
Limitations:
Because of the study's cross-sectional design, causality could not be established between the predictors and SA, and the retrospective data collection approach may introduce recall bias.
