Paralysis Disease Prediction Using Machine Learning

Abstract

Stroke, spinal cord damage, and neurological disease are common causes of paralysis, a significant global public health issue. To reduce the chance of permanent impairment and enhance patient outcomes, paralysis must be detected early. Based on symptoms and demographic information, an early machine learning-based prediction model was presented in this work to classify likely causes of paralysis. A number of models, including Support Vector Machine, XGBoost, Random Forest, and Logistic Regression, were tested. The most accurate model, Random Forest, which has an accuracy of 97.15%, was deployed. Features like email, report generation, a patient input form, and admin dashboards to manage patients and show data results were are included in the web application produced with Streamlit. Overall, the results demonstrated that utilizing AI in clinical settings could help support clinical decision-making, facilitate the construction of diagnostic tools and measurements, and encourage and improve early detection.

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