ANALYZING THE PATIENT DATA AND PREDICTING THE HEART STROKE USING MACHINE LEARNING TECHNIQUES
Cardiovascular strokes have recently eclipsed all other causes of death as the leading cause of death in industrialized, undeveloped, and developing countries. The majority of strokes occur when the brain and heart are abruptly compelled to travel in different directions. If people can identify the warning signs as soon as feasible, the severity of strokes can be decreased. This study proposes an early detection of stroke problems utilizing multiple machine learning algorithms, including hypertension, body mass index, cardiovascular disease, average sugar level, smoking habits, prior stroke, and age. The logistic regression, SVM, Multinomial Naive Bayes, Random Forest, and Decision Tree classifiers were among the ten classifiers that were trained utilizing these high-feature features. The majority of the time, input comes in the form of numerical data for various parameters, and the results are produced in real-time.
Data, Machine learning, Heart stroke, Classification, Plaque, Coronary artery, cardiovascular disease