Importance of Biomarkers and Demographic Data for Alzheimer’s Disease Detection in the Early Stage

Type of Dementia is a Neuro-Degenerative disorder that affects memory and causes difficulty in problem solving and thinking is. Instead of recognizing AD in clinical trials, research in MCI is likely to move the threshold of recognition to a stage earlier in the disease process to allow intervention at an earlier point. At this point, treatments and drugs can either halt or reduce the development of AD. As the outcome, it's critical to diagnose AD at the Mild Cognitive Impairment stage as soon as possible. This research suggests a unique method for early AD detection that combines biomarkers, clinical data, and MRI imaging. The ADNI provided the data set for the Cognitive Normal cases and Mild Cognitive Impairment cases. The clinical dataset includes information on every participant, including demographics, physical exam results, and cognitive test results. To improve the accuracy of AD detection, a range of clinical and genetic data, including CSF biomarkers and amyl-beta biomarkers, are taken into account. In addition to the features derived from the MRI data, clinical data is incorporated for improved classification outcomes. After including each of the chosen biomarkers, a 91% accuracy rate is attained.