Volume 2 - Issue 2, March - April 2026

As a Acute Respiratory Distress Syndrome (ARDS) remains a critical respiratory disease in which timely assessment of a patient’s status can influence clinical interventions and overall survival. This study introduces a two-stage analytical framework designed to predict the condition of a patient using clinical indicators that are measured routinely. The system first uses a deep neural network to derive meaningful internal representations from 22 input attributes, capturing patterns that are not easily visible in their raw form. These learned features are then evaluated by a Random Forest classifier, which determines whether the patient is in a normal or potentially critical state. The model was developed using a data set of 1000 patient records and is integrated into a web-based interface that generates results instantly while maintaining a history of predictions for administrative review. In addition to providing rapid assessment, the platform ensures consistency by storing patient-wise records that can help clinicians monitor progression over time. The dual-stage arrangement reduces prediction noise, improves generalization, and performs more reliably than conventional single-model techniques. Overall, the results suggest that the proposed hybrid neuro-forest framework can support early ARDS.

Varshitha B R, Thunga Mahathi Reddy, Varshini Uday Shet, Dr.S Seetha,"A Dual-Stage Neuro-Forest Framework for Acute Respiratory Distress Syndrome-ARDS Patient Condition Prediction" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(1476-1484) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.