Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

• Published in: Applied sciences 2023-07, Vol.13 (13), p.7833
• Main Authors: Alqahtani, Nayef, Alam, Shadab, Aqeel, Ibrahim, Shuaib, Mohammed, Mohsen Khormi, Ibrahim, Khan, Surbhi Bhatia, Malibari, Areej A
• Format: Article
• Language: English
• Subjects: Accuracy; Advertising executives; Algorithms; alzheimer; Alzheimer's disease; Anomalies; Artificial intelligence; Belief networks; Biomarkers; Brain research; Classification; Comorbidity; DBN; Deep Belief Network; Deep learning; Dementia; Dementia disorders; Development and progression; Diagnosis; Diagnostic imaging; Disease detection; healthcare; Internet of Things; Machine learning; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Medical research; Medicine, Experimental; Morphology; Neural networks; Neuroimaging; Older people; Optimization; Patients; Portable equipment; Support vector machines
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13137833

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.