OPTIMIZING DEMENTIA PREDICTION: A COMPARATIVE PERFORMANCE STUDY OF ML AND DL

Dementia, a progressive neurological disease that impairs cognitive ability, is one of the most important worldwide health issues. With the projected number of affected people to exceed 130 million by 2050, an early and accurate diagnosis is essential to improve patient outcomes. However, conventional diagnostic methods including cognitive evaluations, neuroimaging, and clinical evaluations are time consuming, expensive, and often subjective, stalling timely intervention and increasing the strain on healthcare systems. In order to overcome these obstacles, this work predicts dementia using a novel fusion of deep learning (DL) and machine learning (ML). We investigated a variety of models, such as autoencoders, support vector machines (SVMs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and other machine learning-based classifiers. To enhance predictive robustness, we propose a hybrid ensemble stacking classifier that integrates multiple base classifiers with a metaclassifier. This ensemble approach effectively harnesses the strengths of different models, significantly improving diagnostic accuracy and reliability. This work can facilitate early detection, enable personalized treatment strategies, and ultimately improve the quality of care for people at risk for dementia. To evaluate our model, a dataset of patients with dementia was used. The hybrid ensemble stacking classifier reached 100% compared to the remaining models which were in the range of 62.33% to 97.67%.