Research in brain stroke prediction is very crucial as it can lead to the development of early detection techniques and interventions that can enhance the prognosis for stroke victims. Early detection and intervention can help to minimise the damage caused by a stroke, reduce the risk of long-term complications, and enhance the general quality of life for people who have survived a stroke. Additionally, research in stroke prediction can help to identify risk factors and improve understanding of the underlying causes of stroke, which can lead to the development of better prevention strategies. Research on brain stroke prediction is ongoing and has led to the development of various models and tools for predicting the risk of stroke and detecting it early. However, the implementation and use of these tools in clinical practise vary depending on several factors, such as the availability of resources, the specific healthcare system, and the level of awareness and acceptance of these tools among healthcare providers and patients. In general, risk prediction models may be used to quickly identify individuals at high risk of stroke and target them for preventive interventions, such as lifestyle changes, medication management, and screenings. Early detection tools can be used to quickly identify stroke symptoms and initiate appropriate treatment, which can improve outcomes for stroke patients. However, it is important to note that research and development of these models and tools are ongoing, and their use in clinical practise is constantly evaluated and updated. It may take time for these tools to be widely adopted in clinical practise and to see their real-world impact. This research paper focuses on predicting brain stroke occurrence using a range of machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Bernoulli Naive Bayes (BNB), and a Voting Classifier. The main emphasis of this research was to compare the effectiveness of oversampling techniques, as the data was significantly imbalanced. We have used evaluation metrics in this research to assess the accuracy, precision, recall, and other key performance indicators of a model's predictions. Area under the Receiver Operating Characteristics Curve (AUC), minority class accuracy, and majority class accuracy were used to assess the approaches.

Diabetes Mellitus is a significant world health and early detection is of paramount significance since it decreases the complications and enables medical intervention in time. The paper is a comparison between the predictive accuracy of the eight Machine Learning classifiers: Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, Naive Bayes, k-Nearest Neighbors (k-NN), and an Ensemble model on the Pima Indian Diabetes dataset and a collection of clinical-biological patient records. Performance evaluation was conducted using Precision, Recall, F1-Score, and the Area Under the ROC Curve (AUC-ROC). The findings show that a significant difference was observed among the models, with SVM (AUC-ROC: 0.8648) and the Logistic Regression (AUC-ROC: 0.8638) having the best discriminative ability. A comparable study found that Logistic Regression had the highest Precision (0.7632), indicating fewer false-positive predictions, whereas Decision Tree had the highest Recall (0.7447), indicating greater sensitivity in detecting diabetes cases. The ensemble learning produced the best overall performance (AUC-ROC: 0.8709), suggesting that combining predictions from multiple models increases reliability and generalization. On the other hand, k-NN performed worst due to sensitivity to noise and the number of features. In general, the results provide evidence of the high potential of linear-margin and ensemble-based models to structured clinical data and would be a robust foundation of clinical decision support systems, which further help to broaden the role of ML-based analytics in early diabetes diagnosis and preventive health care planning.