<![CDATA[Bleeding risk assessment is essential in clinical decision-making, especially for patients undergoing frequent blood transfusions. This study presents a machine learning approach combining K-Means clustering and Gaussian Naive Bayes classification to assess bleeding risk based on clinical and transfusion history data. Patients were categorised into K-Means clusters, with the ideal number of clusters established by the Elbow Method and Silhouette Score. PCA visualisation demonstrated distinct distinctions among clusters. Cluster 0 contained patients with higher transfusion volume and frequency, showing significantly higher bleeding risk. Subsequently, the Naive Bayes classifier was trained on clinical features to predict bleeding risk and categorized into two risk levels. The model achieved 85.45 percent accuracy on training data and 86.67 percent on testing data, with the highest predictive accuracy observed in Cluster 0 (95.65 percent). These results highlight the potential of combining unsupervised and supervised learning techniques to enhance bleeding risk stratification and support better transfusion management.]]>
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