Abstract: Stroke is a critical and life-threatening medical condition that necessitates early detection and intervention to mitigate its impact. This project presents a stroke prediction model using the K- Nearest Neighbors (KNN) algorithm, a popular machine learning technique known for its simplicity and effectiveness in classification tasks. In KNN algorithm is applied to classify dataset into two categories. First is at high risk of stroke and second is at low risk of stroke. The objective of this project is to develop a reliable and accurate prediction system that can assist healthcare professionals in identifying individuals at risk of stroke. The dataset used in this project comprises various demographic, clinical, and lifestyle features of a diverse group of individuals, including age, gender, hypertension status, marital status, heart disease history, work type, smoking habits, and more. The project findings indicate that the KNN-based stroke prediction model achieves promising results in terms of accuracy, sensitivity, and specificity. This suggests that KNN can be a valuable tool for identifying individuals who may be at risk of stroke, allowing for early intervention and preventive measures to be taken.
Keywords: stroke, k-nearest neighbors, logistic regression, random forest, machine learning algorithms
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DOI:
10.17148/IJARCCE.2024.134210
