Machine Learmusht for Brain Stroke Prediction

Abstract

Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. Machine learmusht (ML) techniques have been extensively used in the healthcare industry to build predictive models for various medical conditions, including brain stroke, heart stroke and diabetes disease. In this paper, we present an advanced stroke detection algorithm for predicting the occurrence of stroke. We used a dataset contaimusht detail of important parameters which are responsible for the brain stroke like: Age: Body Mass Index (BMI): Gender: Heart Disease: Smoking Status etc, to develop a predictive model. The dataset was preprocessed to handle missing values, handle categorical features and to balance the dataset. We used different classification algorithms such as Naïve Bayes, logistic regression, XgBoost, decision trees, AdaBoost, K-Nearest Neighbor, random forests, Voting classifier and support vector machines to develop our predictive model. The evaluation of the models was conducted using several metrics such as accuracy, F1-score, recall, precision. Moreover an additional metrics parameter is calculated in this paper known as Specificity which was not calculated in earlier studies. Our results showed that the Support Vector Machine algorithm outperformed other models, achieving an accuracy of 99.5%, precision of 99.9% , recall of 99.1%, F1-score of 99.5% and specificity of 99%.