Predicting Guillain-Barre Syndrome: A Machine Learning Approach using Nerve Conduction Study

Guillain-Barre Syndrome (GBS) is a rare yet potentially life-threatening neural condition caused by an immune system attack on the peripheral nerve system that quickly causes muscular weakness, numbness and in severe cases it leads to paralysis. So, Timely diagnosis and intervention are crucial for improving patient outcomes. In this study, it aims to develop a model to predict Guillain-Barre Syndrome (GBS) to assist healthcare professionals in early diagnosing GBS by analyzing nerve conduction studies (NCS) data. The model uses key parameters from NCS, such as Latency, Amplitude, and Conduction Velocity, comparing these values against established normative values to assess the likelihood of GBS. The model provides a binary classification indicating whether a patient is GBS positive or negative. Utilizing machine learning algorithm like logistic regression, the project plans to upgrade the precision and efficiency of GBS diagnosis. This enhanced diagnostic capability has the potential to enable earlier detection and more effective treatment, which leads to improved patient outcomes.