<![CDATA[This research focuses on the application of decision tree methods for identifying the risk of stroke among young adults. Stroke is a significant health concern globally, often leading to long-term disability or death. Identifying individuals at high risk can help in early intervention and prevention strategies. We employed a decision tree algorithm to analyze various risk factors, such as hypertension, diabetes, smoking habits, and physical inactivity. The data was collected from a healthcare database, consisting of young adults aged 18 to 40 years. Our results demonstrate that the decision tree model is effective in classifying individuals with a high risk of stroke, with an accuracy rate of 67,71%. This study suggests that decision tree algorithms can be a valuable tool in clinical settings for early identification and management of stroke risk in young adults. Keywords: decision tree, stroke risk, young adults, machine learning, healthcare]]>
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