• G.Rishitha
• D.Balaram
• G.Sai Kumar
• B.Chandrika

Stroke, one of the most dangerous illnesses in the world, causes a large number of deaths, either directly or indirectly. Machine learning plays a crucial role in the prediction of strokes. It has the potential to employ multiple machine learning algorithms, like Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, and Logistic Regression. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Early prediction of a stroke can save a few lives. The Health Care Stroke Dataset, which includes parameters like age, gender, hypertension, heart diseases, stroke, work type, BMI, smoking status, average glucose level, marriage status, residence type, and stroke, has been our working tool. The accuracy of each algorithm was assessed following data preprocessing, and subsequent comparisons were made to identify the algorithm with the highest accuracy in stroke prediction. Based on the comparison results, a Graphical User Interface individual is at risk of a stroke by analyzing the manual input provided by the user.

Random Forest, Decision Tree, Support Vector Machine Machine Learning, GUI, Logistic Regression, KNN.

"Machine Learning Algorithms for Forecasting Brain Stroke Risk", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.b508-b511, April-2024, Available :http://www.jetir.org/papers/JETIR2404159.pdf

"Machine Learning Algorithms for Forecasting Brain Stroke Risk", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppb508-b511, April-2024, Available at : http://www.jetir.org/papers/JETIR2404159.pdf