Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 11 3 2026 05 06 308 324 Sahana S Madras Christian College Kumarapandiyan G Madras Christian College 2025 02 12 2025 05 13 Introduction: Clinical diagnosis highlights the essential need to assess biomarker performance for effective disease screening and diagnosis. The Receiver Operating Characteristic (ROC) curve serves as a fundamental tool for assessing and interpreting biomarker effectiveness. Numerous models and techniques have been developed to analyze biomarkers in binary classification settings (Non-Diseased vs. Diseased). This research article seeks to expand the binary classification framework to a three-class scenario, incorporating Diseased, Suspicious, and Non-Diseased categories under a Log-Normal distribution. Methods: It introduces a three-class Log-Normal ROC model based on a Parametric approach, deriving metrics such as Volume Under the ROC Surface (VUS) and Asymptotic Variance, as well as an alternative Non-Parametric approach. The model was validated using simulated data generated for the underlying distribution, and a real-life dataset was used to fit the VUS and ROC curves. Results: The simulation study was conducted using four sets with varying parameters. In the fourth set, the Non-Parametric VUS (0.9966) exceeded the Parametric VUS (0.8058), though the difference was smaller compared to the other sets. The low Standard Error (SE) (0.0472) across all sets indicates high precision in the estimates. Additionally, for the real-life (The multiple sclerosis (ms) disease) dataset the VUS value is 0.6782 which gives moderate fit of the model. Conclusion: In this study, we derived the asymptotic variance and VUS for the Log-Normal distribution using simulated data with varying parameters. The analysis compares diagnostic performance across parameter sets, highlighting the superiority of Non-Parametric VUS over Parametric VUS. Set 4 demonstrated the highest reliability with the lowest standard error (SE = 0.0472). The real-life MS dataset provided a moderate fit to the proposed model. https://jbe.tums.ac.ir/index.php/jbe/article/view/1607