Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 2007-2013· 0 citations· 17 references
Abstract
This Attention Deficit Hyperactivity Disorder (ADHD) remains substantially under-diagnosed among university students despite affecting 2–8% of this population. Campus health services, facing persistent resource constraints, frequently accumulate assessment backlogs of 6–12 months. This paper presents a machine learning framework for automated ADHD pre-screening that combines structured psychometric assessments with natural language processing (NLP)-derived features extracted from free-text clinical self-reports. Drawing on 506 university student responses, we engineer 124 multimodal features spanning four validated instruments, the Adult ADHD Self-Report Scale (ASRS), Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI-II), and Adult Attachment Scale (AAS), together with unstructured diagnostic text. Mutual Information-based feature selection reduces dimensionality to 20 features, yielding a 2% accuracy gain. A comparative evaluation across five classifiers reveals Logistic Regression as the top performer, achieving 81.4% accuracy and an AUC of 0.881. SHAP (SHapley Additive exPlanations) analysis confirms clinical meaningfulness by identifying BAI Item 8 (somatic anxiety), ASRS inattention items, and prior mental health history as the principal risk factors. The system is deployed as an interactive web application that delivers calibrated risk assessments suited to clinical triage in resource limited settings.
Mental health disorders such as depression, anxiety, and post-traumatic stress disorder (PTSD) affect over one billion
people worldwide, yet early detection remains a major clinical challenge. In recent years, text data from social media posts,
clinical notes, and patient surveys has emerged as a rich source of signals for automated mental health screening. However,
most existing machine learning models operate as black boxes, limiting clinical adoption. This paper presents an interpretable
machine learning framework that combines natural language processing (NLP) feature extraction with explainable AI
techniques — specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic
Explanations) — to predict mental health conditions from text while providing transparent, clinically meaningful
explanations. A multi- class classification task involving depression, anxiety, PTSD, and healthy controls is performed on a
dataset of 19,320 labelled text samples. The proposed XGBoost model with SHAP explanations achieves 87.3% accuracy and
an AUC of 0.924, while the fine-tuned BERT model achieves 91.6% accuracy and an AUC of 0.961. Experimental results
demonstrate that interpretability does not significantly compromise predictive performance, enabling trustworthy AI-assisted
mental health screening.
N. Thakur, D. Patil, Pushpa Choudhary· International Journal for Re...· 0 citations
Mental health disorders such as anxiety, depression, and Mild Cognitive Impairment (MCI) are increasingly prevalent among young adults aged 18–30, significantly affecting academic performance, cognitive functioning, and overall wellbeing. Traditional diagnostic approaches depend on subjective assessments and limited clinical observations, making early and accurate detection challenging. To address these limitations, this research proposes an interpretable deep learning-based multimodal method for comprehensive mental health prediction and personalized intervention. The framework integrates heterogeneous data sources, including demographic, cognitive, behavioral, physiological, and neurocognitive indicators collected from clinical settings. Data preprocessing includes imputation, normalization, encoding, and text transformation. A Cross-Directional Feature Learning Network (CDFLN) is employed for robust multimodal feature extraction, followed by a Multi-model Progressive Dense Self-Attention for Cross Domain (MPDSA-CD) architecture for classification of anxiety, depression, and MCI, along with cognitive risk and severity assessment. Model performance is further enhanced by the Starfish Optimization Algorithm for hyperparameter tuning and parameter refinement. To ensure clinical transparency, SHapley Additive exPlanations (SHAP) are utilized to interpret model predictions and identify key risk factors influencing mental health outcomes. The proposed method achieves an accuracy of 99.8%, precision of 99.7%, recall of 99.9%, and F1-score of 99.8%, demonstrating strong robustness, generalization ability, and clinical applicability for early detection and effective psychological intervention in young adults.
Rupali D. Kasar, Garima Shukla· Frontiers in Signal Processi...· 0 citations
Machine learning can support diagnosticians in this effort, as demonstrated here utilizing multiple rating scales, the TASI, and the TAP, but there is a risk for bias when using machine learning and as such, no algorithm should replace expert clinical judgment.
Aaron J. Kaat, Ashlynn Campagna, Hannah Feiner et al.· JCPP Advances· 0 citations
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition requiring early and accurate identification to optimize outcomes. The Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) is widely used to assess ADHD symptoms; however, its length may limit feasibility in large-scale screening. This study applied a multi-algorithm machine-learning framework to refine the 18 core ADHD items of the SNAP-IV by identifying the most predictive items through cross-model consensus ranking while preserving balanced symptom construct representation. Data were drawn from the Taiwan National Epidemiological Study of Child Mental Disorders (410 ADHD, 3,607 controls) and an independent National Taiwan University Hospital cohort (676 ADHD, 374 controls). Across ten classifiers optimized for screening with priority on sensitivity, reduced subsets comprising 4 parent-reported and 6 teacher-reported items retained robust predictive performance across cohorts. Confirmatory factor analysis supported the structural validity of the two-factor (Inattention/Hyperactivity-Impulsivity) shortened scales, with strong latent reliability (McDonald's omega). A machine learning-derived, construct-balanced SNAP-IV short form provides an efficient and psychometrically sound tool for ADHD screening.
Chung-Yuan Cheng, H. Chiang, C. Shang et al.· Assessment (Odessa, Fla.)· 0 citations
It is shown that con-textual transformer/LLM models yield more reliable macro-level performance under imbalance than TF–IDF baselines, particularly for semantically adja- cent classes.
Nehal Shah, Mehul Barot· International journal of com...· 0 citations
Automated analysis of speech and language provides a critical opportunity for developing a scalable tool for general medical settings to aid in psychiatric diagnosis and triage. In a cross-diagnostic highly comorbid sample, we evaluated the contribution of speech and language features to diagnostic classification through a hierarchical, dichotomous approach: first distinguishing healthy volunteers (HV) vs. participants with any psychiatric disorder (Split 1), then identifying those with serious mental illness (SMI) vs. other psychiatric disorders (Split 2). Speech was collected from 266 participants via picture description, verbal fluency, paragraph reading, and open-ended verbal journaling tasks. We extracted 640 interpretable features spanning acoustic, temporal, lexical, syntactic, discourse, and coherence domains. LightGBM models were trained with 5-fold cross-validation comparing all combinations of speech tasks, and SHAP values were plotted for feature importance. For Split 1, the best-performing model achieved F1=0.865 combining picture description and journaling tasks, with picture description alone reaching F1=0.830. For Split 2, performance was moderate (F1=0.626), with picture description also as the best single task. Top features for Split 1 included amplitude instability (shimmer) and restricted pitch variance; Split 2 was influenced by articulation rate and filled pauses. This study represents a novel classification approach in a naturalistic, clinically complex sample. Findings suggest that a brief, explainable speech-based assessment may be able to identify individuals who need further evaluation for psychiatric disorders. External validation, bias auditing, and deployment studies are warranted to assess clinical impact.