Performance Analysis of Various Machine Learning Approaches in Stroke Prediction

2020/09/17 by Md. Shafiul Azam, Md. Habibullah, Humayan Kabir Rana · 1 citation
Health Professions · #Artificial Intelligence in Healthcare

paper · pdf · doi:10.5120/ijca2020920740

openalex publication_date 2020/09/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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.

Cited by

• Predictive Performance of Artificial Intelligence Models for Stroke Risk Stratification: A Systematic Review and Meta-Analysis

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