Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Artificial Intelligence in Healthcare · Machine Learning in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, College of Engineering, King Faisal University, Al Ahsa 31982, Saudi Arabia
College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia
Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford M5 4WT, UK
Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Funding
Princess Nourah bint Abdulrahman University
References (48)
Show source-provided references
1. The Leading Causes of Death in the US for 202010.1001/jama.2021.5469 ↗
2. Das, R., Rauf, A., Akhter, S., Islam, M.N., Emran, T.B., Mitra, S., Khan, I.N., and Mubarak, M.S. (2021). Role of Withaferin A and Its Derivatives in the Management of Alzheimer’s Disease: Recent Trends and Future Perspectives. Molecules, 26.10.3390/molecules26123696 ↗
3. Potential New Approaches for Diagnosis of Alzheimer’s Disease and Related Dementias10.3389/fneur.2020.00496 ↗
4. A Systematic Review of MRI Neuroimaging for Education Research10.3389/fpsyg.2021.617599 ↗
5. Alam, S., Shuaib, M., Ahmad, S., Jayakody, D.N.K., Muthanna, A., Bharany, S., and Elgendy, I.A. (2022). Blockchain-Based Solutions Supporting Reliable Healthcare for Fog Computing and Internet of Medical Things (IoMT) Integration. Sustainability, 14.10.3390/su142215312 ↗
6. A Machine Learning Approach to Screen for Preclinical Alzheimer’s Disease10.1016/j.neurobiolaging.2021.04.024 ↗
7. Gao, Z., Pan, X., Shao, J., Jiang, X., Su, Z., Jin, K., and Ye, J. (2022). Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning. Br. J. Ophthalmol., 321472.10.1136/bjo-2022-321472 ↗
8. Battineni, G., Sagaro, G.G., Chinatalapudi, N., and Amenta, F. (2020). Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis. J. Pers. Med., 10.10.3390/jpm10020021 ↗
9. A Comparison of Machine Learning Methods for Survival Analysis of High-Dimensional Clinical Data for Dementia Prediction10.1038/s41598-020-77220-w ↗
10. Machine Learning Models to Predict Onset of Dementia: A Label Learning Approach10.1016/j.trci.2019.10.006 ↗
11. A multivariate adaptive gradient algorithm with reduced tuning efforts10.1016/j.neunet.2022.05.016 ↗
12. A dynamically stabilized recurrent neural network10.1007/s11063-021-10676-7 ↗
13. Deep Learning in Alzheimer’s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data10.3389/fnagi.2019.00220 ↗
14. Context aware emotion detection from low resource urdu language using deep neural network10.1145/3528576 ↗
15. An Overview of Deep Learning in Medical Imaging Focusing on MRI10.1016/j.zemedi.2018.11.002 ↗
16. Comparative Machine Learning Approach in Dementia Patient Classification Using Principal Component Analysis
17. Devising an Interpretable Calibrated Scale to Quantitatively Assess the Dementia Stage of Subjects with Alzheimer’s Disease: A Machine Learning Approach10.1016/j.imu.2016.12.004 ↗
18. Lian, Z., Zeng, Q., Wang, W., Gadekallu, T.R., and Su, C. (2022). Blockchain-Based Two-Stage Federated Learning with Non-IID Data in IoMT System. IEEE Trans. Comput. Soc. Syst.10.1109/TCSS.2022.3216802 ↗
19. Judith, A.M., Priya, S.B., Mahendran, R.K., Gadekallu, T.R., and Ambati, L.S. (2022). Two-phase classification: ANN and A-SVM classifiers on motor imagery BCI. Asian J. Control.
20. Alam, S., Bhatia, S., Shuaib, M., Khubrani, M.M., Alfayez, F., Malibari, A.A., and Ahmad, S. (2023). An Overview of Blockchain and IoT Integration for Secure and Reliable Health Records Monitoring. Sustainability, 15.10.3390/su15075660 ↗
21. Analysis and Design of Surgical Instrument Localization Algorithm
22. Singh, A., Joshi, K., Alam, S., Bharany, S., Shuaib, M., and Ahmad, S. (2022, January 23–24). Internet of Things-Based Integrated Remote Electronic Health Surveillance and Alert System: A Review. Proceedings of the 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), Bhopal, India.10.1109/CCET56606.2022.10080629 ↗
23. ℓ2,1 − ℓ1 Regularized Nonlinear Multi-Task Representation Learning Based Cognitive Performance Prediction of Alzheimer’s Disease10.1016/j.patcog.2018.01.028 ↗
24. A Deep Feature-Based Real-Time System for Alzheimer Disease Stage Detection10.1007/s11042-020-09087-y ↗
25. Feng, C., Elazab, A., Yang, P., Wang, T., Lei, B., and Xiao, X. (2018, January 16). 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer’s Disease Diagnosis. Proceedings of the PRedictive Intelligence in MEdicine: First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain.10.1007/978-3-030-00320-3_17 ↗
26. Multimodal Deep Learning Models for Early Detection of Alzheimer’s Disease Stage10.1038/s41598-020-74399-w ↗
27. AI-Atroshi, C., Rene Beulah, J., Singamaneni, K.K., Pretty Diana Cyril, C., Neelakandan, S., and Velmurugan, S. (2022). Automated Speech Based Evaluation of Mild Cognitive Impairment and Alzheimer’s Disease Detection Using with Deep Belief Network Model. Int. J. Healthc. Manag., 1–11.10.1080/20479700.2022.2097764 ↗
28. A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State FMRI and Residual Neural Networks10.1007/s10916-019-1475-2 ↗
29. Mehmood, A., Maqsood, M., Bashir, M., and Shuyuan, Y. (2020). A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease. Brain Sci., 10.10.3390/brainsci10020084 ↗
30. Convolutional Neural Network Based Alzheimer’s Disease Classification from Magnetic Resonance Brain Images10.1016/j.cogsys.2018.12.015 ↗
31. Sarraf, S., and Tofighi, G. (2016). Classification of Alzheimer’s Disease Using Fmri Data and Deep Learning Convolutional Neural Networks. arXiv.
32. A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection10.1109/ACCESS.2019.2932786 ↗
33. Aderghal, K., Khvostikov, A., Krylov, A., Benois-Pineau, J., Afdel, K., and Catheline, G. (2018, January 18–21). Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning. Proceedings of the 2018 IEEE 31st international symposium on computer-based medical systems (CBMS), Karlstad, Sweden.10.1109/CBMS.2018.00067 ↗
34. A Parameter-Efficient Deep Learning Approach to Predict Conversion from Mild Cognitive Impairment to Alzheimer’s Disease10.1016/j.neuroimage.2019.01.031 ↗
35. Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease10.1016/j.neucom.2018.12.018 ↗
36. Longitudinal Clinical Score Prediction in Alzheimer’s Disease with Soft-Split Sparse Regression Based Random Forest10.1016/j.neurobiolaging.2016.07.005 ↗
37. Kundaram, S.S., and Pathak, K.C. (2019, January 11–12). Deep Learning-Based Alzheimer Disease Detection. Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems: MCCS 2019, Ranchi, India.
38. Vyas, A.H., Mehta, M.A., Kotecha, K., Pandya, S., Alazab, M., and Gadekallu, T.R. (2022). Tear Film Breakup Time-Based Dry Eye Disease Detection Using Convolutional Neural Network. Neural Comput. Appl., 1–19.10.1007/s00521-022-07652-0 ↗
39. Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net10.3389/fpubh.2022.858327 ↗
40. Deep Learning-Based Sentiment Analysis and Topic Modeling on Tourism during Covid-19 Pandemic10.3389/fcomp.2021.775368 ↗
41. Tripathy, B.K., Parimala, M., and Reddy, G.T. (2021). Data Analytics in Biomedical Engineering and Healthcare, Elsevier.
42. Multivariate Approach for Alzheimer’s Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization10.3233/JAD-170069 ↗
43. Korolev, S., Safiullin, A., Belyaev, M., and Dodonova, Y. (2017, January 18–21). Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification. Proceedings of the 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), Melbourne, Australia.10.1109/ISBI.2017.7950647 ↗
44. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain10.1148/radiol.2018180958 ↗
45. Structural MRI-Based Detection of Alzheimer’s Disease Using Feature Ranking and Classification Error10.1016/j.cmpb.2016.09.019 ↗
46. (2023, January 11). Alzheimer’s Disease Neuroimaging Initiative (ADNI). Availabel online: https://adni.loni.usc.edu/.
47. Transfer Learning Using Freeze Features for Alzheimer Neurological Disorder Detection Using ADNI Dataset10.1007/s00530-021-00797-3 ↗
48. A Mayfly Optimization Algorithm10.1016/j.cie.2020.106559 ↗