Early Onset Detection of Alzheimer's Disease Based on Intelligent Techniques

1. Introduction

Alzheimer's disease (AD) is a nervous system problem resulting into memory loss which is short-term, agitation, and delusional thinking. It is occasionally mistaken as stress or ageing. Dementia is recognized as a set of symptoms characterized by a progressive loss of memory or other thinking and reasoning abilities, rather than as a specific condition. In reality, elderly people are largely affected by this disease. The study shows that 2% of dementia sufferers are aged 65 or younger. Dementia affects over 50 million people globally and nearly 10 million new cases identified annually. The number of dementia patients is estimated to exceed 75 million by 2030, costing society almost $2 trillion (Prince M. et.al, 2015). In today's society, dementia has a greater impact on healthcare than any other illness. Furthermore, there is a significant problem in diagnosing dementia because there is no standardized test for its early discovery. As a result, the remedy for dementia at the moment does not exist. Many solutions or therapies are available to improve the lives of these patients and the caregivers (WHO 2020a, 2020b). Alzheimer's disease impacted 50 million individuals in 2018. In the USA, this disease is the sixth biggest reason for death. Physical and neurological examinations can be used to diagnose Alzheimer's disease, but they can be costly and time consuming. The symptoms of this disease often appear gradually and intensify over time, becoming more serious and interfering with daily tasks. However, detecting this illness during its onset, before most of its symptoms appear, is challenging (WHO 2020a, Patterson, C. 2018). Alzheimer's disease prediction at pre-symptomatic phases is recommended so that it can slow down the disease development. The Multi Slice Multi Echo (MSME) score and a manual examination of the Magnetic Resonance Imaging (MRI) image are presently used to diagnose Alzheimer's disease (Janghel, R., 2020, Kumar, U. 2019). This may entail studying hundreds of brain tissue plates, which would be time-consuming and costly. AD is difficult to anticipate in its early phases. A therapy administered early in the course of AD gives better results and leads to minor damage than a treatment administered later as the disease progresses. Lack of early detection can rapidly deteriorate a patient's mental state and advancement to AD. Many researchers are working to invent techniques that can use different technologies for timely prediction of mild cognitive impairment(MCI). These techniques can slow down the progression of the disease as well as can help the patients for effective management of the disease and can help to improve the patients' quality of life. Various computational intelligent methods are being used to forecast or discover diagnostic indicators for Alzheimer's disease. Researchers have utilised AI approaches to collect and analyse data to anticipate Alzheimer's disease. Technologies such as augmented reality, wearable sensors, kinematics analysis have also been mentioned as technologies for developing digital biomarkers for Alzheimer's disease. As there is no well known cure for dementia, technological solutions for the early feasible diagnosis of cognitive decline are required. There is also a need to maximise the efficacy of current novel therapeutic options to delay pathological cognitive ageing. MCI can be difficult to recognise due to many sets of diagnostic criteria and the necessity for long-term follow-up. Furthermore, MCI may precede other types of dementia but does not results in dementia in a large population of people. It is also very difficult for the doctors to identify which patients require a complete cognitive screening, because neuropsychological test batteries are time-consuming and require professional administration. Thus a diagnostic tool should be able to identify early indicators of cognitive impairment, as well as it should be non-invasive, practical, and scalable for use in clinics worldwide.