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Kondalarao Punati is an Associate Professor in the Faculty of Electronics and Communication Engineering at Hyderabad Institute of Technology and Management, Hyderabad, India. His academic responsibilities include teaching and engineering research. His professional role contributes to higher education in electronics and communication engineering. His chapter contribution addresses computational approaches to stroke risk prediction.
Cerebrovascular accidents represent a significant worldwide health issue and are a primary contributor to mortality and chronic impairment. ML provides avenues for examining clinical, demographic, laboratory, and lifestyle information to discern patterns linked to stroke risk. Techniques include logistic regression, decision trees, random forests, support vector machines, gradient boosting may be utilised for stroke prediction and risk assessment. This chapter offers an exhaustive analysis of stroke prediction utilising machine learning, addressing clinical risk factors, dataset selection, preprocessing, feature engineering, model construction, interpretability, evaluation metrics, and clinical decision support. The suggested approach establishes a basis for creating reproducible and clinically accountable stroke risk prediction systems. Such systems ought to enhance conventional clinical evaluations and require validation in suitable populations prior to clinical implementation.