Using Machine Learning and the HAMD-24 Scale to Predict Suicide Ideation in Depressed Patients

Aug 2, 2026

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This study identifies determinants of suicidal ideation and builds an early-risk prediction model for depressed patients using the Hamilton Depression Scale (HAMD-24) with machine learning. The analysis includes 374 depression outpatients, classifying suicidal ideation status via the Beck Suicide Ideation (BSI) Questionnaire. Four models are compared—support vector machine, naive Bayes, random forest, and extreme random trees classification—using accuracy, precision, recall, F1, Kappa, Matthew’s correlation coefficient, and AUC. The ERTC model achieves the best performance (accuracy 77.75%, AUC 0.80) and highlights specific HAMD-24 symptom factors.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Objective

Methods

Participants and assessment

Machine learning models and evaluation

Results

Model performance

Key predictors

Conclusion

Introduction

Assessment scales and HAMD-24

Psychology Research and Behavior Management downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Psychology Research and Behavior Management Open Access Full Text Article ORIGINAL RESEARCH Using Machine Learning and the HAMD-24 Scale to Predict Suicide Ideation in Depressed Patients Yun Chen 1 , Zhong-Yi Jiang2 , Guan-Zhong Dong 1 , Wei-Yuan Zhang 1 , Ke Wang 3 , Hai-Yan Yang 1 1Department of Psychology, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, Changzhou, Jiangsu, 213000, People’s Republic of China; 2School of Computer and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, 213000, People’s Republic of China; 3Nursing Teaching and Research Section, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, Changzhou, Jiangsu, 213000, People’s Republic of China Correspondence: Hai-Yan Yang, Department of Psychology, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, No. 68 Ge Lake Middle Road, Wujin District, Changzhou, Jiangsu, 213000, People’s Republic of China, Tel +86 0519-81099988, [Email yanghaiyanp2@126.com](Email yanghaiyanp2@126.com) | Objective: The aim of this study was to identify factors associated with suicidal ideation and to develop a prediction model for early suicide ideation risk using machine learning algorithms based on the Hamilton Depression Scale (HAMD-24) .
Methods: A total of 374 patients with depression were included from the outpatient department of the Psychology Department at the Second People’s Hospital of Changzhou City. Depression severity was assessed using the HAMD-24, while the Beck Suicide Ideation (BSI) Questionnaire (Chinese Version) was employed to categorize patients into those with and without suicidal ideation. Suicide ideation risk in patients with depression was predicted using four machine learning models: support vector machine, naive Bayes classification, random forest, and extreme random trees classification (ERTC) . This superiority is attributed to ERTC’s extreme randomization which mitigates overfitting in high-dimensional symptom data. The models were evaluated based on accuracy, precision, recall, F1 scores, Kappa coefficients, Matthew’s correlation coefficients, and area under the curve values. The optimal model was then selected, and the factors most strongly associated with suicidal ideation using the HAMD-24 were identified and analyzed.
Results: The ERTC model outperformed SVM, NBC and RF (accuracy 77.75%, AUC 0.80), and despair, guilt, inferiority complex, work and interests loss, depression emotions were the strongest predictors of suicidal ideation. Demographically, patients with suicidal ideation were significantly younger and less likely to be using antidepressants. This is likely attributable to its ensemble structure and inherent randomization during node splitting, which enhances robustness against overfitting and improves generalization when handling the complex, potentially non-linear relationships between HAMD-24 items and suicidal ideation.
Conclusion: We identified the optimal model and then analyzed the factors most strongly associated with HAMD-24 suicidal ideation. The ERTC model, demonstrating superior performance, enables early interventions, and reduces suicide rates. Moreover, this model provides a theoretical reference for the development of new scales focused on depression and suicide.
Keywords: depression, machine learning, predictive model, suicidal ideation | | --- | | Introduction
Depression is a prevalent clinical mental illness characterized by a persistent low mood accompanied by varying degrees of cognitive and behavioral changes, often resulting in functional impairment. It can affect a patient’s education, work, and social life, and in severe cases, may lead to suicide.1 A Chinese mental health survey reported a lifetime prevalence of depression disorder in China of 6.8%, with 53.

FAQs

Which scale is used to predict suicidal ideation in this study?

The prediction relies on the Hamilton Depression Scale (HAMD-24) symptom items. Suicidal ideation status is categorized using the Beck Suicide Ideation (BSI) Questionnaire (Chinese Version).

What machine learning models are compared?

The study compares support vector machine, naive Bayes classification, random forest, and extreme random trees classification (ERTC).

What were the strongest predictors of suicidal ideation?

Despair, guilt, inferiority complex, and loss of work and interests are identified as the strongest HAMD-24-associated predictors of suicidal ideation.

Aug 2, 2026

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