A report on early prediction, detection & diagnosis of Alzheimer’s disease

Khilar, Rashmita; Subetha, T.; Kayal, P.

doi:10.47974/JSMS-960

Open Access Research Article

A report on early prediction, detection & diagnosis of Alzheimer’s disease

^*Rashmita KhilarCorresponding authorrashmita.khilar@gmail.comDepartment of Information TechnologySaveetha School of EngineeringChennaiSaveetha Institute of Medical and Technical SciencesIndiaView full profile → , T. Subethasubethiyagu@gmail.comDepartment of Computer Science and EngineeringHyderabad, TelanganaBVRIT Hyderabad College of Engineering for WomenIndiaView full profile → , P. Kayalkayalpaddu@gmail.comDepartment of Computer Science and EngineeringHyderabad, TelanganaBVRIT Hyderabad College of Engineering for WomenIndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 213–223Vol. 26Issue 1December 2022DOI: 10.47974/JSMS-960XML

Published Online:: 31 Dec 2022
Article type:: Research Article
Language:: EN
Article no.:: JSMS-960
Pages:: 213–223

Abstract

Alzheimer’s Disease (AD) is significantly increasing for older adults in the world nowadays. It affects 10% of people over the age of 65 and 50% of people over 85 years of age. The majority of patients live at home and they are cared for by their family and friends. AD disease is an unavoidable neurological type of disorder, where brain cells die slowly and result in memory loss, leading to Dementia. Sometimes AD disease also affects people of lower age groups. There are three stages of Dementia: 1) Mild, 2) Moderate and 3) Severe. Mild and Moderate stages are treated to some point and in the third stage, there is no treatment for the people. Therefore an early diagnosis is required for this type of disease to avoid a significant loss. As a result, early detection and prediction of infection are needed. Apart from medical researchers, researchers from IoT, AI, Machine Learning have also identified various techniques by which the disease can be predicted and the patient can be monitored for their daily activity. In this paper, various routines to diagnose Alzheimer’s disease are analyzed. The interpretation and evaluation techniques available are also listed out to the early diagnosis of the disease. We have also highlighted early detection and prediction techniques and the promising methods are emphasized.

Keywords

Alzheimer’s disease (AD)Dementia Artificial intelligence IoT Prediction techniques

Subject Classifications

SOA3303C45

References

[1] Kanno, K. M., Lamounier, E. A., Cardoso, A., Lopes, E. J., & de Lima, G. F. M. (2018, March). Augmented Reality System for Aiding Mild Alzheimer Patients and Caregivers. In 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 593-594). IEEE.
[2] Nasir, E. M., Sahar, N. M., & Zainudin, A. H. (2016). Development of augmented reality application for dementia patient (DARD). ARPN Journal of Engineering and Applied Sciences.
[3] Aljehani, S. S., Alhazmi, R. A., Aloufi, S. S., Aljehani, B. D., & Abdulrahman, R. (2018, April). iCare: Applying IoT Technology for Monitoring Alzheimer’s Patients. In 2018 1st International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-6). IEEE.
[4] Lazarou, I., Karakostas, A., Stavropoulos, T. G., Tsompanidis, T., Meditskos, G., Kompatsiaris, I., & Tsolaki, M. (2016). A novel and intelligent home monitoring system for care support of elders with cognitive impairment. Journal of Alzheimer’s Disease, 54(4), 1561-1591.
[5] Westerberg, C. E., Lundgren, E. M., Florczak, S. M., Mesulam, M. M., Weintraub, S., Zee, P. C., &Paller, K. A. (2010). Sleep influences the severity of memory disruption in amnestic mild cognitive impairment: results from sleep self-assessment and continuous activity monitoring. Alzheimer disease and associated disorders, 24(4), 325.
[6] Bharucha, A. J., Anand, V., Forlizzi, J., Dew, M. A., Reynolds III, C. F., Stevens, S., &Wactlar, H. (2009). Intelligent assistive technology applications to dementia care: current capabilities, limitations and future challenges. The American Journal of Geriatric Psychiatry, 17(2), 88-104.
[7] Smagula, S., Gujral, S., Capps, C., &Krafty, R. (2019). A systematic review of evidence for a role of rest-activity rhythms in Dementia. Frontiers in psychiatry, 10, 778.
[8] Ienca, M., Fabrice, J., Elger, B., Caon, M., Scoccia Pappagallo, A., Kressig, R. W., &Wangmo, T. (2017). Intelligent assistive technology for Alzheimer’s disease and other dementias: a systematic review. Journal of Alzheimer’s Disease, 56(4), 1301-1340.
[9] Yousaf, K., Mehmood, Z., Awan, I. A., Saba, T., Alharbey, R., Qadah, T., & Alrige, M. A. (2019). A comprehensive study of mobile-health based assistive technology for the healthcare of Dementia and Alzheimer’s disease (AD). Health Care Management Science, 1-23.
[10] Yawale, K., & Thorat, S. (2019, March). Comparative Analysis of Various Technologies used for the People Enduring through Alzheimer’s Disease: A Survey. In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT) (Vol. 1, pp. 1-5). IEEE.
[11] Husebo, B. S., Heintz, H. L., Berge, L. I., Owoyemi, P., Rahman, A. T., &Vahia, I. V. Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review.
[12] Thorpe, J. R., Forchhammer, B. H., & Maier, A. M. (2019). Development of a sensor-based behavioral monitoring solution to support dementia care. JMIR mHealth and uHealth, 7(6), e12013.
[13] Raad, M. W., Sheltami, T. R., & Shakshuki, E. M. (2015, June). Ubiquitous Tele-health System for Elderly Patients with Alzheimer’s. In ANT/SEIT (pp. 685-689).
[14] Ianculescu, M., Alexandru, A., & Tudora, E. (2017, October). A RFID-based tracking approach for building up smart solutions for consumer’s safety. In 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE) (pp. 1-7). IEEE
[15] Ienca, M., Fabrice, J., Elger, B., Caon, M., Scoccia Pappagallo, A., Kressig, R. W., & Wangmo, T. (2017). Intelligent assistive technology for Alzheimer’s disease and other dementias: a systematic review. Journal of Alzheimer’s Disease, 56(4), 1301-1340.
[16] Woodberry, E., Browne, G., Hodges, S., Watson, P., Kapur, N., & Woodberry, K. (2015). The use of a wearable camera improves autobiographical memory in patients with Alzheimer’s disease. Memory, 23(3), 340-349.
[17] Mahoney, E. L., & Mahoney, D. F. (2010). Acceptance of wearable technology by people with Alzheimer’s disease: Issues and accommodations. American Journal of Alzheimer’s Disease & Other Dementias®, 25(6), 527-531.
[18] Thorpe, J., Forchhammer, B. H., & Maier, A. M. (2019). Adapting Mobile and Wearable Technology to Provide Support and Monitoring in Rehabilitation Processing and Information Technology (ISSPIT) (pp. 1-5). IEEE.
[19] Gacem, M. A., Alghlayini, S., Shehieb, W., Saeed, M., Ghazal, A., & Mir, M. (2019, December). Smart Assistive Glasses for Alzheimer’s Patients. In 2019 IEEE International Symposium on Signalfor Dementia: Feasibility Case Series. JMIR Formative Research, 3(4), e12346.
[20] Aruanno, B., & Garzotto, F. (2019). MemHolo: mixed reality experiences for subjects with Alzheimer’s disease. Multimedia Tools and Applications, 78(10), 13517-13537.
[21] Rashmi Parameshachari, Supriya Maganahalli Chandramouli, Building Enhanced Chaotic Map Encryption Method for Medical Information System, Journal of System and Management Sciences, Vol. 11 (2021) No. 1, pp. 176-192 DOI:10.33168/JSMS.2021.0111.
[22] Alzheimer’ Disease and Related Dementias Guideline Development Panel Agency for Health Care Policy and Research, U.S. Department of Health and Human Services Early Identification of Alzheimer’s Disease and Related Dementias: Quick Reference Guide for Clinicians, Journal of Pharmaceutical Care in Pain and Symptom Control.
[23] Vinayak K.Bairagi,Improved epileptic seizure detection using singular spectrum empirical mode decomposition and machine learning approach, Journal of Statistics and Management Systems, Volume 25, 2022, Issue 1.
[24] Anshul Bhatia, Application of extreme learning machine in plant disease prediction for highly imbalanced dataset, Journal of Statistics and Management Systems, Volume 23, 2020 - Issue 6: Intelligent Decision Making using Best Practices of Big Data Technologies (Part-I).
[25] Shikhar Seth, Basant Agarwal,A hybrid deep learning model for detecting diabetic retinopathy, Journal of Statistics and Management Systems, Volume 21, 2018 - Issue 4.

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