Abstract
Stroke is a blood clot or bleeds in the brain, which can make permanent damage that has an effect on mobility, cognition, sight or communication. It is the second leading cause of death worldwide and one of the most life- threatening diseases for persons above 65 years. It damages the brain like “heart attack” which damages the heart. Every 4 minutes someone dies of stroke, but up to 80% of stroke can be prevented if we can identify or predict the occurrence of stroke in its early stage. In this paper, I used different types of machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke data. Four types of machine learning classification algorithms were applied; Linear Regression, Confusion matrices, Random Forest Classifier, and Logistic Regression were used to build the stroke prediction model. Support, Precision, Recall, and F1-score were used to calculate performance measures of machine learning models. The results showed that Random Forest Classifier has achieved the best accuracy at 94 % [1].
Highlights
Migraine is the name given to the condition that people who experience these headaches often have
It was used to train and test models for predicting stroke disease. This dataset includes of 10 independent variables as features and one dependent variable as the class label that is used to predict stroke disease
The results showed that random forest classifier achieved the best accuracy result
Summary
Migraine is the name given to the condition that people who experience these headaches often have. Migraine can range from mild to extremely debilitating and last for just a few hours but some cases can be more severe and last for days. It is one of the most common ailments in the united states, with over 36 million people experiencing them each year. The diagnosis is usually made based on doctor’s practice and knowledge This guides to undesired results and extreme medical loss of treatments provided to patients.
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