Pain Sense: Intelligent Pain Mapping System for Spinal Cord Injuries

Millions of people in the world suffer from the injuries and devastating ailment of spinal cord pain. For correct diagnosis and therapy, professionals must accurately identify spinal cord pain, but this can be a difficult undertaking. The machine learning method suggested in this module uses a dataset of patient medical records, imaging data, and other pertinent data to identify spinal cord pain. To extract valuable features for analysis, the data is pre-processed by utilizing signal processing and feature engineering approaches. The next step is to train a machine learning algorithm to link the retrieved features with the presence or absence of spinal cord pain using supervised learning methods like logistic regression and neural networks. The performance of the proposed algorithm is assessed by using several criteria, including accuracy, precision, recall, and F1-score. The research findings indicate that our method successfully detects spinal cord pain with high accuracy. This project lays the groundwork for future research in this field and shows how machine learning might improve spinal cord pain diagnosis and therapy.