Comprehensive Methodologies For Alzheimer’s Disease Prediction:A Machine Learning Approach

Dementia is mostly caused by Alzheimer's disease, which causes memory loss and significant cognitive deterioration. Effective management of this illness depends on prediction. Using a wealth of patient health data, this study investigates the use of machine learning methods to predict when Alzheimer's disease will manifest. We employed several categorization methods, including logistic regression, decision trees, K-nearest neighbour (KNN), and support vector machines (SVM), bagging and boosting. By combining these approaches, especially focusing on boosting techniques, we aim to improve prediction accuracy. Our results, illustrated through bar graphs, show how well these models work for risk assessment and diagnosis. The comprehensive analysis outlines each model's advantages and disadvantages, offering a detailed comparison of their performance. The results support the expanding field of healthcare machine learning applications, providing valuable insights for future research and potential integration into clinical practice. Ultimately, this work aims to enhance early detection strategies, improving outcomes for those who are susceptible to Alzheimer's disease.