Machine Learning-Based Transcriptomic Diagnosis of Periodontitis

Periodontitis is a common, serious condition often diagnosed too late using traditional methods that can be subjective. To improve detection, we developed an machine learning (ML) tool that analyses genetic activity in gum tissue. Using data from 616 patient samples, we identified five key genes (CSF2RB, COL15A1, MME, NEFM, CYP24A1) that act as biological 'flags' for gum disease. These genes are linked to immune responses that drive gum inflammation. Our ML models - especially two types called Random Forest and XGBoost - perfectly spotted gum disease in initial tests and remained highly accurate in new patient groups. We also created a simple scoring chart (nomogram) to predict individual risk. The genes we found interact with immune cells and vitamin D pathways, revealing new disease mechanisms. This work provides a faster, more objective way to diagnose gum disease and opens doors for personalised treatments.