• Ms.AMRUTHA
• Dr. Shashidhar Kini K

This study presents the development of a Brain Stroke Prediction System using supervised machine learning algorithms to aid in the early diagnosis and prevention of brain strokes. The system uses patient health records with features such as age, gender, hypertension, heart disease, average glucose level, BMI, smoking status, and work type to predict stroke occurrence. The dataset was obtained from open-source repositories like Kaggle and underwent rigorous preprocessing to handle missing values and encode categorical variables. The modeling framework compares the performance of Logistic Regression, K- Nearest Neighbors (KNN), Decision Tree, and Random Forest classifiers using metrics such as accuracy, precision, recall, F1- score, and AUC-ROC.

Brain Stroke, Machine Learning, Classification, Logistic Regression, Random Forest, Health Prediction, Medical Analytics, Feature Engineering

"BRAIN STROKE PREDICTION A Phase 1 Report on Developing a Predictive Model for Brain Stroke Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1184-1187, July-2025, Available :http://www.jetir.org/papers/JETIRGX06229.pdf

"BRAIN STROKE PREDICTION A Phase 1 Report on Developing a Predictive Model for Brain Stroke Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1184-1187, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06229.pdf