JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Predictive Approach for Early Brain stroke diagnosis using various classifiers
Nisha H T, Prashanth A
Predicting early brain injury, such as stroke, is critical in clinical settings for timely intervention and better patient outcomes. The cutting-edge approach combines advanced machine learning techniques, specifically random forest classifiers, logistic regression, and decision tree classifiers, to improve forecasting accuracy. This methodology begins with collecting and preprocessing patient data, with a focus on medical history and clinical measurements to ensure data quality and relevance. Ensemble learning improves performance by utilizing random forest classifiers, which are well- known for their robustness and ability to handle high-dimensional data. Logistic regression generates probabilistic stroke risk outputs and interpretable coefficients, making predictions easier for clinicians to understand and act on. Decision trees provide intuitive visual models for capturing complex, nonlinear relationships in data. The proposed method entails teaching these strategies on ripped datasets until conserving how they do through hyperparameter tuning. Simulations are evaluated comprehensively using metrics such as precision, exactness, and think. The optimal model, which accurately predicts stroke risk in new clientele, provides actionable insights for healthcare providers. This allows to acquire the early identification and treatment, which protects calm health and reduces the severity of possible hazards. This a gauge tool is extremely useful in treatment, allowing for unique treatment plans and much nearer monitoring of high-risk patients, finally enhancing client results through early identification and mitigation.
Brain stroke, Decision tree classifiers, Random Forest, Anaconda prompt