School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Sci Rep. 2024 Aug 29;14(1):20053. doi: 10.1038/s41598-024-70354-1.
A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, and suitable therapies are available. The purpose of this study is to develop a stroke prediction model that will improve stroke prediction effectiveness as well as accuracy. Predicting whether someone is suffering from a stroke or not can be accomplished with this proposed machine learning algorithm. In this research, various machine learning techniques are evaluated for predicting stroke on the healthcare stroke dataset. The feature selection algorithms used here are gradient boosting and random forest, and classifiers include the decision tree classifier, Support Vector Machine (SVM) classifier, logistic regression classifier, gradient boosting classifier, random forest classifier, K neighbors classifier, and Xtreme gradient boosting classifier. In this process, different machine-learning approaches are employed to test predictive methods on different data samples. As a result obtained from the different methods applied, and the comparison of different classification models, the random forest model offers an accuracy rate of 98%.
中风是一种危险的、危及生命的疾病，主要影响 65 岁以上的人群，但不健康的饮食也导致了年轻人中风的发生。如果中风能尽早发现并提供合适的治疗，就可以成功治疗。本研究旨在开发一种中风预测模型，以提高中风预测的有效性和准确性。可以使用这个提议的机器学习算法来预测某人是否患有中风。在这项研究中，评估了各种机器学习技术在医疗保健中风数据集上预测中风的能力。这里使用的特征选择算法是梯度提升和随机森林，分类器包括决策树分类器、支持向量机（SVM）分类器、逻辑回归分类器、梯度提升分类器、随机森林分类器、K 近邻分类器和极端梯度提升分类器。在这个过程中，使用不同的机器学习方法在不同的数据样本上测试预测方法。从应用的不同方法中获得的结果，以及不同分类模型的比较，随机森林模型提供了 98%的准确率。
