Enhancing Epilepsy Diagnosis: Performance Analysis of Machine Learning Ensemble Techniques

Alzheimer's disease risk assessment is a critical task in healthcare, aimed at identifying individuals at risk and enabling early interventions to slow disease progression. Our research concludes that machine learning ensemble techniques offer a promising approach to improving Alzheimer's disease risk assessment, providing healthcare providers with powerful tools to enhance diagnosis and patient care. In this study, we utilized a dataset consisting of Alzheimer’s disease patient records, and we applied various machine learning ensemble classifier techniques such as ExtraTreesClassifier, Voting Classifier, Random Forest Classifier, Stacking Classifier. Our findings suggest that ensemble techniques, particularly ExtraTreesClassifier, significantly outperform individual models in predicting Alzheimer’s disease risk. Th Random Forest Classifier achieved the highest accuracy score of 95.58 %. These results highlight the potential of machine learning ensemble methods in providing reliable and early predictions of Alzheimer’s disease, which can facilitate timely interventions and improve patient outcomes. Our research concludes that machine learning ensemble techniques offer a promising approach to enhancing epilepsy diagnosis, providing healthcare professionals with powerful tools to improve diagnostic accuracy and patient care. The success of these models, especially the ExtraTreesClassifier, demonstrates the capability of machine learning techniques to enhance the accuracy of Alzheimer's disease risk prediction and aid in proactive healthcare management.