arXiv subjects

Rajveer Singh Pall

Publications and source records attributed to Rajveer Singh Pall.

3 recordsLinked to original sources

arXiv2026

Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis

Machine learning-based Type 2 diabetes risk prediction models obtain good internal validation results but lose effectiveness in real-world applications due to deficient external testing and fairness assessment. We developed a multi-dimensional framework evaluating discrimination, calibration, interpretability, and algorithmic fairness on nationally representative populations. An XGBoost model was trained on NHANES 2015-2020 (n=15,685) using eight non-laboratory predictors: age, sex, race/ethnicity, BMI, smoking status, physical activity, history of heart attack, and history of stroke. External validation was performed on BRFSS 2020-2022 (n=1,285,783) under realistic distribution shift. Internal validation showed good discrimination (AUC=0.794, 95% CI 0.788-0.800), with performance loss on external validation (AUC=0.717, relative decrease: -9.7%, p<0.001). Fairness analysis revealed severe bias: elderly adults (>=60) showed AUC=0.607 vs 0.742 for young adults (difference=0.135, p<0.001); obese individuals showed AUC=0.698 vs 0.735 for normal weight (difference=0.037, p<0.001). Gender showed comparable performance (male=0.723 vs female=0.712, p=0.142). Calibration revealed risk overestimation (Brier score=0.123). SHAP analysis identified age, BMI, and physical activity as primary risk drivers. Populations with highest diabetes risk receive the worst algorithmic performance, underscoring the need for fairness-aware, age-stratified deployment strategies before clinical use.

cs.LG↗

arXiv2026

Cross-Platform Generalisation Failure in Mental Health Natural Language Processing: A Five-Axis Fairness Audit of Transformer Models on Social Media

We introduce the Cross-Platform Fairness Evaluation (CPFE) framework -- a five-axis audit protocol covering discriminative performance, calibration, statistical significance, prediction equity, and attribution stability -- and apply it to four transformer models (BERT, RoBERTa, Emotion-DistilRoBERTa, GoEmotions-RoBERTa) trained on a Kaggle mental health corpus (n=35,556) and evaluated on Reddit (n=6,257) and Twitter (n=2,883) test sets with emotion labels mapped to clinical proxies. All three independently evaluated models exhibit consistent and substantial cross-platform AUC degradation (30.3-35.4% on Reddit, 37.9-39.5% on Twitter) relative to within-platform performance (AUC 0.983-0.987), confirmed across five independent training seeds. Calibration failure is concurrent and severe: ECE rises from 0.056-0.060 in-domain to 0.196-0.229 on Reddit and 0.499-0.542 on Twitter. Platform-specific temperature scaling reduces mean ECE by 88.0% without altering discriminative performance (mean |delta AUC|<0.01), confirming separable failure modes. Prediction equity analysis reveals large cross-platform disparities (raw DI < 0.17; prior-shift-adjusted DI: 0.11-0.29 on Reddit), with equalized odds differences of 0.753-0.830 for mental health proxy classes on Reddit and 0.755-0.831 for anxiety on Twitter. Attribution stability analysis shows near-complete vocabulary divergence across platforms (Jaccard J=0 in 14/16 model-class pairs at K=10). These findings support treating cross-platform validation across all five CPFE axes as a standard requirement for mental health NLP systems in heterogeneous environments. In a single-seed fine-tuning experiment, mean AUC improved by 0.216, suggesting target-platform labels provide greater benefit as training signal than as calibration signal.

cs.CL↗

arXiv2026

IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text

We introduce IndiaFinBench, to our knowledge the first publicly available evaluation benchmark for assessing large language model (LLM) performance on Indian financial regulatory text. Existing financial NLP benchmarks draw exclusively from Western financial corpora (SEC filings, US earnings reports, English-language financial news), leaving a significant gap in coverage of non-Western regulatory frameworks. IndiaFinBench addresses this gap with 406 expert-annotated question-answer pairs drawn from 192 documents sourced from the Securities and Exchange Board of India (SEBI) and the Reserve Bank of India (RBI), spanning four task types: regulatory interpretation (174 items), numerical reasoning (92 items), contradiction detection (62 items), and temporal reasoning (78 items). Annotation quality is validated through a model-based secondary pass (kappa=0.918 on contradiction detection; 90.7% overall agreement on a 150-item subset) and a 180-item human inter-annotator agreement study across three annotation rounds (kappa=0.645 on contradiction detection; 77.2% overall agreement; 44.3% benchmark coverage). We evaluate twelve models under zero-shot conditions, with accuracy ranging from 70.4% (Gemma 4 E4B) to 89.7% (Gemini 2.5 Flash). All models substantially outperform a non-specialist human baseline of 69.0%. Numerical reasoning is the most discriminative task, with a 35.9 percentage-point spread across models. Bootstrap significance testing (10,000 resamples) reveals three statistically distinct performance tiers. The dataset, evaluation code, and all model outputs are available at https://github.com/rajveerpall/IndiaFinBench

cs.CL↗