Abstract

The progressive nature and early identification requirements of Alzheimer’s disease (AD) provide an immense challenge in healthcare. The present study introduces a novel ensemble learning technique for detecting AD, using cutting-edge deep learning (DL) and classic machine learning (ML) techniques. The feature extraction process is carried out with YOLOv7 and EfficientNet B3 models, which effectively capture spatial and semantic information from brain imaging data. CatBoost and XGBoost are used as base learners, using gradient-boosting capabilities for classification. In order to improve the accuracy of predictions, support vector machines are used as meta-learners to effectively merge the results of the base models. We performed trials on a dataset from the Kaggle repository and achieved a remarkable average accuracy of 99.8%. Our methodology shows the effectiveness of integrating DL and classic ML methods in detecting AD. The ensemble architecture not only boosts the accuracy of classification but also improves the resilience and generalizability of the model. The study’s results indicate promising directions for advancing the development of precise and dependable diagnostic instruments for AD. The proposed research has the potential to assist medical professionals in identifying the condition at an early stage and planning appropriate interventions and treatments.