Heart Stroke Prediction Using Machine Learning Techniques

Abstract

In today's generation, many people face heart-related diseases and lose their life due to heart attack, stroke, and cardiac arrest. Heart strokes have become a challenging symptom as patients face issues like speech difficulty, face drooping and arm weakness which shows the patient is suffering from heart-related issues. To prevent the deaths due to heart strokes, the new technologies could help to predict the symptoms earlier, which are responsible for heart-related diseases and reduce the number of deaths. The chapter also shows the comparison of different algorithms to differentiate from old traditional methodologies and find the best methodology which gives the best result. In the case of prediction models, parameters like age, gender, BMI, medical history, drinking and smoking habits, level of glucose, type of work the person does, etc., which will help to find the root cause for deaths in heart-related diseases. The use of prediction models, algorithms like random forest, decision tree, k-nearest neighbour, SVM, help to make a comparative analysis and keep a record of accurate models.