GUI BASED PREDICTION OF HEART STROKE STAGES BY SUPERVISED MACHINE LEARNING ALGORITHM

Machine Learning

Heart attacks diseases are considered as the most Prevalent. Stroke Prediction using patient treatment history and health data by applying data mining and machine learning techniques is ongoing struggle for the past decades. The traditional prescient models or methods are as yet not powerful enough in catching the hidden information since it is unequipped for reproducing the multifaceted nature on include portrayal of the therapeutic issue space. In existing system, Vascular state is determined by measuring changes in the pressure wave forms induced through intentional variation in the device generated blood flow.To overcome this problem, predictive analytical techniques for heart stroke using machine learning model applied on given hospital data set. The performance from the given hospital data set with evaluation of classification report and identify the confusion matrix. Focuses on the design of a graphical user interface (GUI) for the prediction of stroke using risk parameters. Data collected from Kaggle data set was successfully trained and tested using Supervised machine learning algorithm.To propose a machine learning-based method to accurately predict the heart stroke by given attributes in the form of best accuracy from comparing supervised classification machine learning algorithms. Additionally, to compare and discuss the performance of various machine learning algorithms from the given healthcare department data set with evaluation classification report, identify the confusion matrix . Result shows that the effectiveness of GUI based the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.

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