Authors : Nnanna, Chidera Egegamuka; Nnanna, Ekedebe; Ajoku, Kingsley Kelechi; Okafor, Chidozie Raymond Patrick; Ozor, Chidinma C
Volume/Issue : Volume 9 - 2024, Issue 4 - April
Google Scholar : https://tinyurl.com/2a3u5kn8
Scribd : https://tinyurl.com/4rtxzs6x
DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR2566
Abstract : Stroke is a significant cause of mortality andmorbidity worldwide, and early detection and preventionof stroke are essential for improving patient outcomes.Machine learning algorithms have been used in recentyears to predict the risk of stroke by leveraging largeamounts of clinical and demographic data. Thedevelopment of a stroke prediction system using RandomForest machine learning algorithm is the main objectiveof this thesis. The primary goal of the project is to increasethe accuracy of stroke detection while addressing theshortcomings of the current system, which include real-time deployment and interpretability issues with logisticregression. The development and use of an ensemblemachine learning-based stroke prediction system,performance optimization through the use of ensemblemachine learning algorithms, performance assessment,and real-time model deployment through the use ofPython Django are among the goals of the research. Thestudy's potential to improve public health by lessening theseverity and consequences of strokes through earlydiagnosis and treatment makes it significant. Datacollection, preprocessing, model selection, evaluation, andreal-time deployment using Python Django are all part ofthe research technique. Our dataset consists of 5110 rowsof tuples and columns with total size of 69kg. Theperformance of our stroke prediction algorithm wasevaluated using confusion metrics-consisting of accuracy,precision, recall and F1-score. At the end of the research,Random Forest model gave an accuracy of 98.5%compared to the existing model logistic regression whichhas 86% accuracy.
Keywords : Machine Learning Algorithms, Preporcessing, Random Forest Model, Confusion Matrix, F-Score Measurement, Stroke Prediction.
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Stroke is a significant cause of mortality andmorbidity worldwide, and early detection and preventionof stroke are essential for improving patient outcomes.Machine learning algorithms have been used in recentyears to predict the risk of stroke by leveraging largeamounts of clinical and demographic data. Thedevelopment of a stroke prediction system using RandomForest machine learning algorithm is the main objectiveof this thesis. The primary goal of the project is to increasethe accuracy of stroke detection while addressing theshortcomings of the current system, which include real-time deployment and interpretability issues with logisticregression. The development and use of an ensemblemachine learning-based stroke prediction system,performance optimization through the use of ensemblemachine learning algorithms, performance assessment,and real-time model deployment through the use ofPython Django are among the goals of the research. Thestudy's potential to improve public health by lessening theseverity and consequences of strokes through earlydiagnosis and treatment makes it significant. Datacollection, preprocessing, model selection, evaluation, andreal-time deployment using Python Django are all part ofthe research technique. Our dataset consists of 5110 rowsof tuples and columns with total size of 69kg. Theperformance of our stroke prediction algorithm wasevaluated using confusion metrics-consisting of accuracy,precision, recall and F1-score. At the end of the research,Random Forest model gave an accuracy of 98.5%compared to the existing model logistic regression whichhas 86% accuracy.
Keywords : Machine Learning Algorithms, Preporcessing, Random Forest Model, Confusion Matrix, F-Score Measurement, Stroke Prediction.