Predictive Healthcare with Integrated EEG and Facial Emotion Recognition using Deep Learning and Transfer Learning

The main objective of this innovative project is to transform modern-day neurological healthcare, additionally pairing and synchronizing EEG signals with facial recognition information for efficient detection of neurological sicknesses. Machine learning algorithms are used for promptitude, ensuring rapid interventions during the course of the disease, and for careful and efficient balancing of the patient’s conditions. The system based on the above-discussed approach is characterized by a patient-centered nature ensuring that trust and ethical liability are the top priorities of the developed model. Such system supported by multiple machine learning algorithms also increase the accuracy of the diagnosis, its potential for early identification, and targeted therapy, ultimately having a beneficial effect on the public health. The researches findings demonstrate that there are convincing proofs that machine learning can be used for predictive healthcare: VGG19 achieved 86.28%, Random Forest Classifier 94.48%, and for epilepsy, Random Forest and Gradient Boosting for brain tumors 92.12% and for Parkinson’s disease 90.52%, respectively. Ensembled EEG analysis had the accuracy of 91.83%. Importantly, a result of 82.51% accuracy was achieved with the final decision-making process using transfer learning with the method of ensembled models and VGG19. Thus, it is possible to conclude that different machine learning algorithms are effective in predictive healthcare and can promote the progress of medical technology to benefit public health.