# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
## 1 Introduction  
When ranked by prevalence, AD ranks third in the US, behind cardiovascular disease and cancer, and is the sixth leading cause of death worldwide [1]. The entorhinal cortex, the hippocampus neocortex, and other brain regions are highly susceptible to neuronal cell death, neurofibrillary tangles, and senile plaques that characterize AD. The number of people living with dementia is expected to rise from its current 75.6 million to 135.5 million by 2050 [2]. For the past fifty years, studies on AD have predicated on including and excluding specific clinical factors. Clinical criteria are only 85% accurate in detecting AD, so a detailedexamination is necessary for a definitive diagnosis [3]. The role of instrumental tests in clinical diagnosis such as detecting cerebral atrophy on a brain scan or measuring the concentration of specific proteins in a patient's blood has grown over time. Quantitative evaluation is intrinsically linked to the creation of novel neuroimaging techniques. Dementia can be detected in its earliest stages with the help of neuroimaging techniques developed by the ADNI. Recent advances in neuroimaging techniques, such as MRI, have allowed researchers to identify and present novel molecular and structural biomarkers for AD. Clinical trials have demonstrated that the diagnostic accuracy of neuroimaging techniques like MRI is improved. It has been speculated that MRI can detect abnormalities in brain morphology related to mild cognitive impairment (MCI) and thus accurately predict the progression from MCI to AD. The aim is to identify structural and molecular hallmarks of AD. Clinical studies have demonstrated increased diagnostic accuracy of neuroimaging modalities like MRI [4]. It has been hypothesized that MRI can reliably predict whether or not a person with MCI will develop AD by detecting the abnormalities in brain morphology that are characteristic of MCI. There has also been a call for research into \"multimodal biological markers,\" which may aid in the initial diagnosis of AD [5]. Electroencephalogram (EEG) data, neuropsychological data, demographic data, APOE4 genotype data, and MRI information were used to train the ML classifier by [PERSON] et al. [6]. The model is educated to recognize the onset of AD and its telltale signs, including amyloid plaques and neuronal degeneration. After five years, EEG can predict neurodegenerative diseases, just as amyloid accumulation and prodromal diseases can be predicted with psychographic and MRI data. This study confirmed previous findings that used ML techniques to predict AD onset successfully. Being able to form opinions rapidly is the end result [7]. Several supervised Ensemble methods were compared and analyzed for their potential use in the classification of AD [8]. Some claims of improved classification accuracy and specificity have been made for boosting models [9], like the generalized boosting model and the gradient boosting machines (GBM). Using ML expertise and the patient's medical records, dementia can also be predicted. Dementia onset prediction using AD patient records over two years using a gradient boosting model (light GBM) was also proposed [10]. In the end, we got an accuracy rate of 87%. The use of recurrent neural networks (RNN) to simulate the development of AD was also proposed [11]. Data assertion and regression methods were used to evaluate it against an alternative RNN model. As a result, when training on unlabeled data, accuracy reached 74%. The inter-data relationship between MRI demographic data and AD can also be learned. Studies have shown that random forest (RF) models perform better than other models, including support vector machine (SVM) models, when using this technique [12]. Deep learning models can forecast the advancement from MCI to AD [13]. Since deep learning models benefit from more information, pre-processing unlabeled data is a good idea [14]. Several studies [15] have shown promise in using deep learning to diagnose AD and detect symptoms.