Early Brain Stroke Detection Using Artificial Intelligence
| dc.contributor.author | MD. Khalilur Rahman | |
| dc.date.accessioned | 2025-11-23T04:02:44Z | |
| dc.date.available | 2025-11-23T04:02:44Z | |
| dc.date.issued | 2024-04-30 | |
| dc.description.abstract | Due to the current environmental conditions and human lifestyle choices, people are impacted by a wide range of diseases nowadays. If such diseases are to be prevented from reaching their terminal phases, early detection and prediction are essential. An enormous financial burden is placed on individuals who suffer from stroke, a cerebrovascular disorder that is one of the main causes of mortality. One of the main risk factors for stroke is health-related behavior, which is gaining importance as a prevention strategy. Many machine learning algorithms that incorporate lifestyle characteristics as predictors to autonomously diagnose stroke have been used to predict the risk of stroke. This work uses five supervised machine learning classifiers to predict strokes: K-Nearest Neighbor Algorithm, Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes. The aforementioned classifiers are trained on the dataset, which consists of 5110 items with 10 attributes, and their performance is assessed using the confusion matrix. The dataset is preprocessed to make it acceptable for prediction. The RF method surpassed all other algorithms in the employed dataset for predicting strokes based on many physiological characteristics, with an accuracy of 95.8%. In contrast to an individual's medical history and level of physical activity, machine learning algorithms may be more useful for the clinical estimation of stroke. Stroke patients need continuous critical care in addition to all of these diagnoses, which can be provided by an interdisciplinary team. Keyword: Artificial Intelligence, Confusion Matrix, Random Forest Classifier, Stroke | |
| dc.identifier.uri | https://dspace.nstu.ac.bd/handle/123456789/108 | |
| dc.title | Early Brain Stroke Detection Using Artificial Intelligence |