Early Identification of Schizophrenia Risk Using IoT-Collected Speech Data and Advanced NLP Algorithms

Schizophrenia, a complicated and long-lasting mental illness, frequently shows mild initial symptoms that can be difficult to identify. This research investigates the capability of combining Internet of Things (IoT)-collected speech data with advanced Natural Language Processing (NLP) algorithms for early detection of schizophrenia risk. We used IoT-enabled devices to record spontaneous speech samples from at-risk individuals. Advanced NLP algorithms were used to analyze speech samples and detect linguistic and acoustic characteristics that suggest early signs of schizophrenia. We use deep learning models to analyze and categorize these characteristics, creating a system for timely intervention. Initial findings indicate that this method combining IoT and NLP can improve the ability to identify individuals at risk early on, providing a useful tool for mental health professionals to implement preventative measures sooner. This research highlights the possibility of enhancing mental health diagnosis and patient results by integrating IoT technology with advanced NLP methods.