Enterprise AI Analysis
A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic
This paper introduces Adaptive Sigma KNN (ASKNN), an improved K-Nearest Neighbors (KNN) model designed for accurate and stable detection of depression and anxiety. ASKNN dynamically adjusts neighbor influence using an adaptive sigma parameter based on local data distribution. It has been rigorously tested on various mental health and medical datasets, demonstrating superior accuracy, precision, recall, and F1-scores compared to traditional KNN and its variants. ASKNN's ability to adapt to complex data distributions and its robustness to noise and class imbalances make it a powerful tool for early intervention and diagnosis in mental health, aligning with global health objectives.
Executive Impact: A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic
This research presents a significant advancement in AI-driven mental health diagnostics, crucial for enterprise healthcare systems and public health initiatives. ASKNN's superior predictive accuracy for depression (91%) and anxiety (84.5%) directly translates into earlier, more reliable diagnoses. The model's robustness and generalizability, validated across multiple datasets, minimize false positives and negatives, ensuring efficient resource allocation and timely patient interventions. This directly supports the United Nations SDG 3, enhancing global well-being by leveraging AI for improved health outcomes at scale.
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Depression Accuracy
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Anxiety Accuracy
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Depression AUC-ROC
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Anxiety AUC-ROC
Deep Analysis & Enterprise Applications
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Model Innovation
Enhanced Accuracy
Statistical Validation
Ethical Implications
Adaptive Sigma KNN (ASKNN)
ASKNN is an advanced variant of the K-Nearest Neighbors (KNN) algorithm that dynamically adjusts the influence of each neighbor based on local data density. Unlike traditional KNN, which uses a constant weighting, ASKNN computes the local density around each test point by estimating the inverse distance to its k-nearest neighbors. This local density then informs an adaptive sigma parameter for a Gaussian function, which calculates neighbor weights. A larger local density (closer neighbors) results in a smaller sigma, localizing neighbor influence, while a smaller local density (sparse neighborhood) leads to a larger sigma, spreading influence. This adaptability allows ASKNN to perform better in datasets with varying data distributions, improving classification precision and robustness, making it a locally sensitive classifier robust to outliers and sparse samples.
Precision and Recall in Mental Health Detection
The ASKNN model consistently demonstrates superior precision and recall across various mental health datasets compared to traditional KNN and its variants. For depression detection, ASKNN achieved 92% precision and 86% recall on proprietary datasets (D1a, D1b), significantly outperforming conventional KNN (89% precision, 82% recall). Similarly, for anxiety detection, ASKNN showed 86% precision and 79% recall. The model's high precision minimizes false positives, which is crucial in clinical settings to avoid unnecessary interventions, while high recall ensures that true positive cases are not missed, which is vital for early and effective treatment of mental health conditions. These results highlight ASKNN's capability to accurately identify relevant cases while maintaining low false alarm rates, making it a reliable diagnostic tool.
Robust Statistical Significance and Effect Size
The performance improvements of ASKNN were statistically validated using Friedman and Wilcoxon signed-rank tests, demonstrating significant differences (p < 0.05) across accuracy, precision, recall, and F1-score compared to other KNN variants. This statistical robustness ensures that ASKNN's superior performance is not due to chance. Furthermore, effect size measures like Cohen's d and Cliff's Delta confirmed the practical significance of these improvements, indicating that ASKNN offers meaningful enhancements over baseline models. This rigorous statistical analysis reinforces ASKNN as a reliable and effective machine learning model for mental health classification, capable of providing actionable insights for clinical decision-making.
Addressing Bias and Generalizability
While ASKNN shows promising results, the research acknowledges limitations inherent in self-report survey data, such as self-selection bias, social desirability, and recall bias, which could affect generalizability. The cross-sectional design limits causal inference. Future work aims to mitigate these biases through stratified random sampling, inclusion of diverse demographic groups, and integration of clinician-conducted diagnostic interviews. This comprehensive approach will enhance ground truth validity and minimize reliance on subjective reporting, ensuring the ethical and robust deployment of AI models in sensitive mental health contexts. Longitudinal studies are also planned to track symptom evolution and provide early warning features.
Key Performance Advantage: Anxiety Detection
ASKNN significantly enhances anxiety detection with a robust accuracy, critical for early intervention in mental health. Its adaptive mechanism allows for precise identification across varied data distributions.
84.5% Anxiety Detection Accuracy (ASKNN)Enterprise Process Flow
Data Collection & Preprocessing
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Local Density Calculation
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Adaptive Sigma Adjustment
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Gaussian Weight Calculation
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Weighted K-NN Classification
Improving Mental Health Outcomes with ASKNN
A major public health concern, mental illnesses such as depression and anxiety have emerged. Early and accurate identification is critical for early intervention and treatment. However, traditional diagnosis relies heavily on clinical interviews, which can be time-consuming, subjective, and largely unavailable in resource-constrained environments. Machine learning methods have emerged as increasingly crucial in mental illness studies since they can process massive data and identify advanced patterns that are associated with mental disorders. The use of machine learning-based methods in the diagnosis of mental illnesses has the potential for expansion of early diagnosis and enhanced treatment accessibility, particularly in the aftermath of the psychological effects of the pandemics.
ASKNN's high accuracy and ability to adapt to diverse patient profiles make it an ideal candidate for integration into digital mental health platforms, enabling timely and objective screening at scale. This supports a proactive approach to mental healthcare, potentially reducing the burden on clinical resources and improving patient access.
ASKNN vs. Other KNN Variants: Key Advantages
ASKNN demonstrates superior performance and adaptability compared to other KNN variants, making it highly effective for complex, real-world data distributions in mental health diagnostics.
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Depression Detection Performance
ASKNN's enhanced model achieves high accuracy in identifying depression cases, crucial for timely interventions and improving patient outcomes.
91.0% Depression Detection Accuracy (ASKNN)Advanced ROI Calculator
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Your AI Implementation Roadmap
A phased approach to integrating ASKNN for mental health detection within your enterprise.
Phase 01: Pilot Program & Data Integration
Identify a pilot department or cohort for initial ASKNN deployment. Focus on secure integration with existing HR/healthcare data systems, ensuring data privacy and compliance. Establish baseline metrics for mental health indicators.
Phase 02: Model Customization & Validation
Fine-tune ASKNN with your specific organizational data to enhance predictive accuracy for your employee demographics. Conduct internal validation studies, comparing ASKNN's performance against traditional screening methods.
Phase 03: Scaled Deployment & Training
Gradually roll out ASKNN across the organization. Implement comprehensive training for healthcare professionals and HR staff on utilizing the AI insights for early intervention and support. Develop clear protocols for action based on ASKNN's outputs.
Phase 04: Continuous Monitoring & Refinement
Establish a continuous monitoring framework for ASKNN's performance and impact on employee well-being. Regularly update the model with new data and adapt to evolving mental health trends, ensuring long-term effectiveness and relevance.
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