# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
## 1 Introduction
With the help of a deep learning model that is both precise and thorough in identifying the first signs of AD, the disease could be diagnosed and treated much sooner. Discretizing MRI data and handling outliers effectively can improve ML classification accuracy. Reportedly, people with dementia can be accurately categorized using supervised models in conjunction with feature selection [16]. In another investigation [17], multifactor affiliation analysis was used to categorize patients according to the interrelationships of features. This method excels beyond classification trees and generic distribution zones in classifying patients and efficiency. These methods failed to show how crucial it is to use data-centric ML techniques and embrace model-boosting knowledge to turn inefficient learners into efficient ones and enhance model performance [18]. IoT applications are spreading throughout the medical industry [19]. IoT can be used to sense real-time patient data and other environmental detail and can be sent for further processing [20]. It helps in fast data processing, relief from manual work and avoiding mistakes while digitizingthe records [21]. IoT-enabled portable MR imaging device captures real-time patient MR images and identifies anomalies in MRI scans for detecting and classifying AD [22]. The more recent algorithm is known as mayfly optimization (MOA.) This algorithm could be used to identify male and female mayflies in a group. Each one of them updates in its own special way. If a person's current position was extremely far from the best contender or the historically best trajectories, their progress toward the best position slowed in the MO algorithm's initial iteration. It is easy to see how such actions could immediately slow the rate of convergence. To boost the efficiency of MOA algorithms, we suggest rewriting the updating equations that have been applied throughout this paper. In this paper, we make significant advancements over previous work in both the accuracy with which DBN can classify the status of a patient and its ability to deal with voice features. DBN use many processing layers to model higher-level abstractions in complex data structures. Weighted connections link the processing layers together, but the layers themselves are isolated. It allows us to view it as a generative graphical model with many hidden nested units, similar to the structure of a tree. These networks can only learn as much as their trainers allow when given supervised and unsupervised training. The current survey's contributions are summarized as follows:  
1. Evaluate how well the current model can foresee the development of AD using the ADNI database. 2. In addition, we proposed a framework for AD/non-AD classification of dementia subjects using longitudinal brain MRI features and DBN with an MOA. 3. In contrast to the current literature, our DBN-MOA models are optimized by considering a wide range of low-cost time-series features, such as patients' comorbidities, cognitive scores, medication histories, and demographics. The following topics make up the study's subsections: In Section 2, we provided a review of the recent related works. Part III, look at the fundamentals of DBN and proposed a DBN- MOA approach for Alzheimer detection with detailed algorithms. Section 4 discusses the proposed model and compared the output results with the similar approaches provided in other studies. The paper's final Section 5 discusses the proposed model's implications and directions for future research.