dhilu164

Gene-Expression-Monitoring

Stroke-Pridiction

Stroke is one of the causes of death and the leading cause of serious long-term debilitation in the world. This paper compares various machine learning algorithms for stroke prediction on the Stroke Dataset. Six types of machine learning classification algorithms were applied; Gauss Naive Bayes, Logis- tic Regression, Decision Trees, Random Forest Classifier, Light Gradient Boosting and XGBoost were used to build different stroke prediction models. Hyperparameter tuning and validation sets have been applied to machine learning algorithms to improve results. We used sensitivity, specificity, AUC, Precision, accuracy, Recall and F1-measure to calculate performance metrics for machine learning models. The results show that the Random Forest classifier achieved the highest accuracy at 97%.

Web_Development

otp-verification

Job_Dashboard

dhilu164/Web_Development

dhilu164/Gene-Expression-Monitoring

dhilu164/Stroke-Pridiction

Stroke is one of the causes of death and the leading cause of serious long-term debilitation in the world. This paper compares various machine learning algorithms for stroke prediction on the Stroke Dataset. Six types of machine learning classification algorithms were applied; Gauss Naive Bayes, Logis- tic Regression, Decision Trees, Random Forest Classifier, Light Gradient Boosting and XGBoost were used to build different stroke prediction models. Hyperparameter tuning and validation sets have been applied to machine learning algorithms to improve results. We used sensitivity, specificity, AUC, Precision, accuracy, Recall and F1-measure to calculate performance metrics for machine learning models. The results show that the Random Forest classifier achieved the highest accuracy at 97%.