Stroke Prediction using Logistic Regression

According to World Health Organisation (WHO), strokes are the second leading cause of death and the third leading cause of disability globally. Stroke is the sudden death of some brain cells due to lack of oxygen when the blood flow to the brain is lost by blockage or rupture of an artery to the brain, it is also a leading cause of dementia and depression

The objective of this study is to construct a prediction model for predicting stroke and to assess the accuracy of the model. We will explore seven different models to see which produces reliable and repeatable results.

The dataset used for this project is the?Stroke Prediction Dataset?from Kaggle. This dataset is used to predict whether a patient is likely to get a stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relevant information about the patient.

out of 5110 observations, The number of persons who have not suffered from a stroke, or who are healthy, is 4861, while the number of patients who have suffered from a stroke is 249.

Stroke affects around 8% of total patients who have formerly smoked, 4% of total patients who have never smoked, and 5% of total patients who smoke.

female patients suffering from a stroke at the age of 80 years are more likely than male patients suffering from a stroke at the same age