Earley Stage Brain Stroke Detection System Using Machine Learning

Earley Stage Brain Stroke Detection System Using Machine Learning

Yash Ramesh Patil,

Akshay Titame,

Yogesh Patil,

Dr. Umesh Pawar.

Department of Computer Science & Engineering, SANDIP University.

ABSTRACT
The purpose of this research is to develop an early detection system for brain strokes using machine learning. Strokes require immediate medical intervention to minimize damage, making early detection crucial. This paper reviews existing models and proposes a machine learning-based model capable of analyzing patient data and predicting stroke risks with high accuracy. The methodology includes data preprocessing, feature selection, model training, and validation. Our results indicate a significant improvement in early detection rates, suggesting that machine learning models could assist in early intervention and reduce stroke-related fatalities.

Keywords: Early Stroke Detection, Machine Learning in Healthcare, Predictive Modelling, Stroke Prediction System, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest Classifier