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Alzheimer's disease (AD) is one of the most frequent neurodegenerative disorders in the elderly subjects. Since early detection can prevent or delay cognitive decline in the older subjects, it is desirable to develop effectual protocols for the diagnosis of the disease. Most of the existing diagnostic tools fail to improvise timely disease prognosis in susceptible patients. Keeping this fact into consideration, we developed a cognitive-based 3-tiered machine learning (ML) algorithm employing baseline characteristics to predict AD or mild cognitive impairment (MCI) to construct psychometric test results. Earlier machine learning based AD diagnosis methods used a binary or multinomial classification technique. We relied on the development of a sophisticated hybrid cognitive ML algorithm that provides an accurate and precise prediction of the disease. We built an ML model using cognitive and demographic data. The …