Predictive Model of Early Ischemic Stroke in Diabetic Patients

Ischemic stroke is a life-threatening medical emergency caused by a lack of blood flow to the brain. Diabetic patients are at an increased risk of ischemic stroke due to factors like impaired metabolism and inflammation. Early prediction and intervention are crucial for mitigating the severe consequences of stroke. This paper presents a predictive modeling approach using machine learning techniques to identify diabetic patients at high risk of early ischemic stroke. The model incorporates diverse factors such as age, alcohol consumption, hypertension, diabetes status, smoking habits, work type, gender, and stroke history. The results demonstrate robust predictive capabilities, with age, hypertension, and diabetes emerging as significant contributors to the risk assessment. The inclusion of lifestyle factors adds granularity, enabling a comprehensive understanding of individual risk profiles. While internal validation underscores the model's stability, external validation across diverse cohorts is essential. These findings hold promise for enhancing early intervention strategies and emphasize the importance of personalized risk assessment in mitigating the impact of ischemic strokes in diabetic populations.