Healthcare
A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic
This study introduces Adaptive Sigma KNN (ASKNN), an enhanced version of the K-Nearest Neighbors algorithm, designed for more accurate and stable detection of mental health disorders like depression and anxiety. Tested on a comprehensive dataset including psychological questionnaires and medical benchmarks, ASKNN dynamically adjusts neighbor influence, leading to superior predictive accuracy and robust performance compared to traditional KNN and its variants. The findings highlight ASKNN's potential for early intervention and its contribution to global mental health initiatives under SDG 3.
Executive Impact
Our advanced AI-driven analysis of "A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic" reveals critical insights for healthcare enterprises. ASKNN's superior accuracy in mental health diagnosis offers transformative potential for patient care and operational efficiency.
Depression Accuracy
Anxiety Accuracy
Depression AUC-ROC
Anxiety AUC-ROC
Deep Analysis & Enterprise Applications
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ASKNN's core innovation lies in its adaptive sigma parameter, which dynamically adjusts neighbor influence based on local data density. This allows the model to better adapt to complex, non-uniform data distributions typical in real-world mental health datasets. By enhancing predictive accuracy and stability, ASKNN offers a robust solution for early and effective detection of depression and anxiety, outperforming traditional KNN methods. Its ability to classify mild, moderate, and severe cases with high precision and recall is critical for timely clinical interventions.
ASKNN consistently outperforms standard KNN and its variants (AKNN, FKNN, KMKNN, WKNN, GMDKNN, MKNN, EKNN) across multiple datasets and metrics. Statistical tests, including Friedman and Wilcoxon signed-rank tests, confirm ASKNN's significant improvements in accuracy, precision, recall, and F1-score. This superior performance, particularly in high-dimensional and imbalanced datasets, positions ASKNN as a more reliable and generalizable tool for mental health classification, offering a substantial competitive advantage over existing KNN-based algorithms.
By significantly improving the accuracy of depression and anxiety detection, ASKNN directly contributes to the United Nations Sustainable Development Goal 3: Good Health and Well-being. Early and accurate diagnosis facilitates prompt intervention and treatment, reducing the global burden of mental illnesses. ASKNN, as an AI-powered diagnostic tool, makes mental health screening more accessible and scalable, especially in resource-constrained environments, thereby advancing global efforts towards better mental health outcomes for all.
Key Finding: Adaptive Precision in Depression Detection
ASKNN demonstrates superior precision in classifying depression levels, achieving high accuracy in positive case identification while minimizing false positives, crucial for reliable mental health diagnoses.
92% Depression Precision (ASKNN)Enterprise Process Flow
Data Collection (Questionnaires)
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Data Preprocessing (Imputation, Encoding)
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ASKNN Model Training (Adaptive Sigma)
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Mental Health Disorder Classification
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Performance Evaluation (Accuracy, Precision, Recall, F1)
| Model | Key Features | Benefits |
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| ASKNN (Proposed) |
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Case Study: Mental Health Screening in University Settings
Challenge: Universities faced a surge in student mental health issues post-COVID-19, with traditional diagnostic methods being time-consuming and subjective, leading to delays in intervention and support.
Solution: Implementation of an AI-powered screening system using ASKNN to process self-reported psychological questionnaire data (BDI, BAI) for early detection of depression and anxiety among students.
Result: The ASKNN model achieved 91.00% accuracy for depression and 84.50% for anxiety, enabling rapid and precise identification of at-risk students. This facilitated timely mental health support and resource allocation, significantly improving student well-being and reducing the burden on counseling services. The model's interpretability also allowed clinicians to understand the factors contributing to risk.
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Your AI Implementation Roadmap
A phased approach to integrate AI solutions seamlessly into your operations.
Phase 1: Discovery & Strategy
Duration: 2-4 Weeks. Initial assessment of current systems, data infrastructure, and specific mental health screening needs. Define clear objectives and success metrics for AI integration. Develop a tailored strategy aligned with clinical workflows and patient privacy regulations.
Phase 2: Pilot Program & Integration
Duration: 8-12 Weeks. Implement ASKNN in a controlled pilot environment using a subset of real data. Integrate the model with existing electronic health records (EHR) or screening platforms. Conduct rigorous testing and validation with clinical supervision to ensure accuracy and user acceptance.
Phase 3: Full-Scale Deployment & Optimization
Duration: Ongoing. Roll out ASKNN across the entire target population (e.g., all university students, patient cohorts). Monitor performance continuously, gather feedback, and retrain the model with new data as needed. Implement advanced features like multimodal data input for even higher diagnostic accuracy and generalizability.
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