Comprehensive Performance Analysis of Neurodegenerative disease Incidence in the Females of 60-96 year Age Group

Neurodegenerative diseases such as Alzheimer’s disease (AD) and dementia are gradually becoming more prevalent chronic diseases, characterized by the decline in cognitive and behavioral symptoms. Machine learning (ML) is revolutionising almost all domains of our life, including the clinical system. The application of ML has the potential to enormously augment the reach of neurodegenerative care thus building it more proficient. Throughout the globe, there is a massive burden of AD and dementia cases; which denotes an exclusive set of difficulties. This provides us with an exceptional opportunity in terms of the impending convenience of data. Harnessing this data using ML tools and techniques, can put scientists and physicians in the lead research position in this area. The objective of this study was to develop an efficient prognostic ML model with high-performance metrics to better identify female candidate subjects at risk of having AD and dementia. This paper portrays our latest contribution to the advancement in neurodegenerative disorders. The study was based on two diverse datasets. The results have been discussed employing seven performance evaluation measures i.e. accuracy, precision, recall, F-measure, Receiver Operating Characteristic area, Kappa statistic, and Root Mean Squared Error. Also, comprehensive performance analysis has been carried out later in the study. The experiment had shown a high accuracy of 98.90 for the AD recognition and 99.60 for the dementia prognosis.