Rapid Health Anomaly Detection in IoBNT Systems Using Molecular and Terahertz Communication

Early detection of diseases in the human body is critical for accurate diagnosis and efficient treatment. Recent breakthroughs in nanotechnology, AI, and communication networks allow nano-biosensors to monitor the body and detect disease symptoms earlier. Molecular communication (MC) and 6G-Terahertz (THz) communication technologies are expected to offer a viable approach to early diagnosis procedures. By integrating these technologies, we envision a future diagnostic and medicine delivery system that is revolutionary. In this study, we propose a future disease diagnostic Internet of Bio-Nano Things (IoBNT) system that integrates MC, THz, and machine learning (ML) techniques to detect disease biomarkers remotely. MC allows nanobots to communicate using biochemical molecules as information carriers. The THz communication carries intra-body signals from the nanodevices to the external environment. To analyze the system, we collect the real-world ovarian cancer biomarker dataset and use different ML models to predict and evaluate the model's accuracy. According to the prediction model accuracy, early diagnosis of the disease will significantly improve patient treatment and without side-effects.