A COMPARATIVE ANALYSIS OF MACHINE LEARNING CLASSIFIERS FOR STROKE PREDICTION

Stroke, a major global health concern, is characterized by sudden neurological deficits and impaired cerebral function. Advancements in technology and the integration of medical records offer opportunities to enhance stroke care and diagnosis. By mining and analyzing electronic health records, valuable insights into the interdependencies of stroke risk factors can be gained, aiding in prediction. This research provides a comprehensive review of the application of machine learning classifiers in stroke prediction, considering various techniques, features, and performance measures utilized in previous studies. The novelty of this work is to emphasize the potential of machine learning classifiers in improving stroke prediction, with a focus on feature selection, data pre-processing, and model evaluation. The aim is to shed light on the strengths and limitations of different classifiers, including Decision Trees, AdaBoost, and Gradient Boost, and their performance metrics in stroke prediction. By achieving this goal, effective stroke risk assessment models can be developed, leading to improved patient outcomes through early intervention and targeted preventive measures. The findings reveal that machine learning classifiers, particularly AdaBoost and Gradient Boost, show promising performance in stroke prediction. These classifiers demonstrate high recall rates and balanced F1 scores, indicating their efficacy in identifying individuals at risk of stroke. This research contributes to the growing body of knowledge in stroke prediction and highlights the potential of machine learning techniques in enhancing stroke care. The integration of machine learning classifiers with stroke prediction holds great promise in improving patient outcomes. By harnessing the power of electronic health records and utilizing appropriate techniques and features, healthcare providers can enhance their ability to identify and intervene in stroke cases, ultimately leading to better preventive measures and care strategies.