Abstract:

Machine learning models can identify patterns and subtle changes in patient data, enabling early detection of Parkinson's disease. Early diagnosis is crucial as it allows for timely intervention and improved management of the condition. Machine learning algorithms can quickly process data and provide screening results within a short period. This can significantly speed up the diagnostic process and help healthcare professionals make timely decisions. Decision trees provide binary outcomes and do not naturally produce probabilistic predictions. Probability estimates can be obtained using techniques like logistic regression on the decision tree outputs or using ensemble methods. Parkinson's disease datasets may suffer from imbalanced classes, where the number of healthy individuals significantly outweighs the number of patients. Ensemble stacking can help address this issue by combining models that are specifically designed to handle imbalanced data or by adjusting the model's decision threshold to accommodate the class imbalance.

Description:

Machine Learning, Ensemble Stacking, Decision Tree, Weight Optimization, probabilistic predictions