Advances in Machine Learning for Stroke Prediction: A Comprehensive Review

The comparative study in this review paper covered several phases of predictive modelling and examining the efficacy of different models for stroke prediction. Thorough exploratory data analysis (EDA) and strict data pretreatment were part of the initial look into, which made sure the model building process had a strong foundation. A thorough examination of the prediction performance of a various machine learning algorithms was made possible by the analysis, which included everything from traditional classifiers to state-of-the-art ensemble techniques. Lazy Predictor was used for benchmarking and initial model selection, which made it simple to identify the best-performing algorithms. These models' predictive accuracy was further improved by subsequent fine-tuning, which improved their appropriateness for stroke prediction tasks. In addition, the investigation of ensemble learning strategies cleared the way for the creation of hybrid models, which combine the advantages of distinct classifiers to produce better prediction results. The comparison analysis yielded useful insights that influence the selection of classification models for stroke prediction by healthcare researchers and practitioners. Stakeholders can embrace and apply ML approaches for stroke prediction with more knowledge if the study's findings are summarized. This will lead to improved medical outcomes and therapy.