Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population

Area of Science:

• Public Health
• Computational Psychiatry
• Epidemiology

Background:

• Suicide ideation poses a significant public health challenge.
• Early identification of individuals at risk is crucial for intervention.

Purpose of the Study:

• To develop and validate a machine learning model for predicting suicide ideation in the general population.
• To assess the model's performance using a large-scale national health survey dataset.

Main Methods:

• A random forest model was trained using data from the Korea National Health & Nutrition Examination Survey (KNHANES).
• Recursive feature elimination and 10-fold cross-validation were employed for feature selection and model optimization.
• The model was validated on a separate test set and the entire study population.

Main Results:

• The prediction model demonstrated strong performance with an area under the receiver operating characteristic curve (AUC) of 0.85 in the test set.
• The model achieved an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807 in predicting suicide ideators among the total sample.

Conclusions:

• Machine learning offers a viable approach for screening suicide risk within the general population.
• Further research is needed to enhance prediction accuracy and clinical utility.