Machine Learning-Driven Stroke Prediction: Improving Patient Outcomes Through Early Diagnosis

Stroke is a cause of death and long-term disability and requires timely diagnosis and effective preventive treatment. Recent advances in machine learning (ML) techniques have shown promising results in predicting stroke risk. This research article explores the application of ML algorithms in the accurate and early prediction of stroke occurrence, thereby making effective interventions and reducing morbidity and mortality. This study used an extensive database of various individuals' demographics, medical histories, lifestyles, and physical measurements. Special engineering techniques process the data and remove the associated assumptions. To train predictive models, use various machine learning algorithms such as logistic regression, random forests, and decision tree are used by authors to conduct the experiment. A stroke is an extreme medical condition that could result in death. Early identification of stroke chance is essential for stopping lengthy-term harm to the mind. In this paper, authors examine a stroke prediction dataset and the usage of machine learning algorithms. Our outcomes display that the random forest and decision tree algorithm outperforms different algorithms, attaining an accuracy of 91%. Our studies demonstrate the potential of the device's getting-to-know strategies in predicting stroke threat and enhancing affected person effects.