The majority of methods work by reducing the dimensionality of the feature space. A variety of methodologies have been suggested for patient classification (AD and/or MCI Cuingnet et al., 2011). Models based on machine learning provide a promising opportunity for developing tools that can detect disease progression. Machine learning methodologies are often utilized for the examination of said high dimensional data (Cuingnet et al., 2011). Because of AD complexity, image analysis in combination with pre-existing in vivo biomarkers (amyloid-ОІ (AОІ), tau, etc.) is a more reliable diagnostic tool (Westman et al., 2011). Advances in neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) scans enable scientists to search for AD patterns in the entire brain. In the era of big data, analysis of large volumes of data requires progressive approaches. Predicting the exact point when patients convert from the prodromal stage of the disease (MCI) to AD would be extremely beneficial in identifying novel mechanisms of disease prevention. Because of the complexity of the disease, one specific drug or treatment intervention appears unlikely to successfully treat the disease. The majority of current approaches aim to help patients manage behavioral symptoms and impede others such as memory loss and cognitive decline. Therefore, identifying subtle brain changes that occur during the MCI-to-AD conversion as early as possible could be the key to the development of more effective treatment plans. Like any other disease, early diagnosis is important. Approximatively 20вЂ“40% of MCI patients convert to AD (MCI converters-MCIc Grassi et al., 2019). MCI patients can either remain stable at this stage of the disease or convert to AD. MCI is also known as the stage between normal cognitive ageing and dementia and is often thought of as the prodromal stage of AD (Grassi et al., 2019). This is partly because although MCI patients appear to have neurological deficits, their symptoms are not advanced enough to meet the AD criteria.

Researchers find it challenging to classify the MCI stage. AD broadly includes three stages: preclinical AD, mild cognitive impairment (MCI) and dementia (Grassi et al., 2019).

Specifically, it is believed that AD begins 20 years before the onset of symptoms. Regardless the onset of the disease, it is important to note that it takes years for the symptoms to manifest. Recent studies propose that AD is a middle-life disease (Ritchie et al., 2015). It usually affects the older population, but early onset AD is still possible (Ritchie et al., 2015). AlzheimerвЂ™s disease (AD) is an irreversible and progressive neurodegenerative disease as well as the most common form of dementia.

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