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Performance Analysis of Various Machine Learning Approaches in Stroke Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 21 |
| Year of Publication: 2020 |
| Authors: Md. Shafiul Azam, Md. Habibullah, Humayan Kabir Rana |
10.5120/ijca2020920740
|
Md. Shafiul Azam, Md. Habibullah, Humayan Kabir Rana . Performance Analysis of Various Machine Learning Approaches in Stroke Prediction. International Journal of Computer Applications. 175, 21 ( Sep 2020), 11-15. DOI=10.5120/ijca2020920740
Abstract
Stroke is one of the most life threatening diseases. Now a day the difficulty of Stroke is a global health concern. Once a stroke disease occurs, it is not only matter of huge medical care and permanent disability but also can eventually lead to death. The most of the strokes can be prevent if we can identify or predict the occurrence of stroke in its early stage. In this situation, machine learning can be a hope. It plays a vital role in the prediction of diseases in health care industry. In this paper, the various machine learning approaches like Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) are employed to predict the risk of stroke whether a patient will be affected by stroke or not. The main purpose of this research is to highlight the employing of machine learning algorithms in prediction of stroke risk and analysis the performance of these algorithms. This research also analyzed the significant features of datasets to predict the stroke risk.
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