Predictive Analytics with Multimodal Inputs for Mental Health Assessment

Abstract

Artificial intelligence has changed mental health care by providing predictive analytics scales and thus helps to prevent a crisis before it occurs. Due to increased mental health problems worldwide, AI-based models can interpret a variety of data types, such as clinical history, social determinants, patient-reported measures, or electroencephalogram brain activity (EEG). Our model can provide more insight into the mental state of a patient by incorporating EEG signals to measure patterns of neural activity that indicate a number of conditions including depression, anxiety, and emotional distress. We used more powerful data mining in retrieving pattern or information in complex data and data fusion integration of EEG with other health variables to provide prediction. These technological breakthroughs are incredibly promising, at the same time providing new ethical issues of privacy, data processing, and using just algorithms. It highlights the revolutionary impact of AI in the mental sphere, specifying the significance of EEG data and combining multiple sources combined with the issues of transferring the technique to clinical practice. Along this guideline, the accuracy rate of our proposed model was 96 percent, much higher than conventional approaches in forecasting people at risk of developing mental illnesses. Integrating machine learning algorithms and natural language processing into EEG data enhanced the accuracy of diagnostic procedures and permitted individual treatment plans. Moreover, active surveillance with the help of wearable gadgets and mobile applications enables immediate response in order to prevent a crisis.