Afreen Khan ¹ Swaleha Zubair ² Mohammed Shuaib ³ Abdullah Sheneamer ^3* Shadab Alam ³ Basem Assiri ³

• ¹ Integral University, Lucknow, Uttar Pradesh, India
• ² Aligarh Muslim University, Aligarh, Uttar Pradesh, India
• ³ Jazan University, Jizan, Saudi Arabia

Machine learning (ML) algorithms and statistical modelling offer a potential solution to offset the challenge in diagnosing early Alzheimer's disease (AD) by leveraging multiple data sources and combining information on neuropsychological, genetic, and biomarker indicators. In this study, the optimization of predictive models for the diagnosis of AD pathologies was carried out using a set of baseline features. The model performance was improved by incorporating additional variables associated with patient medications and protein biomarkers into the model. In the present study, early AD was diagnosed by taking into account characteristics related to whether or not a patient was taking specific medications and a significant protein as a predictor of Amyloid-Beta (Aβ), tau, and ptau (AT(N)) levels among participants. We propose a hybrid-clinical model that runs multiple ML models in parallel and then takes the majority's votes, enhancing the accuracy. We also assessed the significance of three cerebrospinal fluid (CSF) biomarkers, Aβ, tau, and ptau in the diagnosis of AD. We proposed that a hybrid-clinical model be used to simulate the MRI-based data, with five diagnostic groups of individuals (cognitively normal, significant subjective memory concern, early mildly cognitively impaired, late mildly cognitively impaired, and AD), with further refinement which includes preclinical characteristics of the disorder. It is noteworthy that we aimed to construct a pipeline design that incorporates comprehensive methodologies to detect Alzheimer's over wide ranging input values and variables in the current study. The proposed design builds a Meta-Model for four different sets of criteria. The set criteria are as follows: to diagnose from baseline features, baseline and drug features, baseline and protein features, and baseline, medication and drug features. We were able to attain a maximum accuracy of 97.60% for baseline and protein data. We observed that the constructed model functioned effectively when all five drugs were included and when any single drug was used to diagnose the response variable. Interestingly, the constructed Meta-Model worked well when all three protein biomarkers were included, as well as when a single protein biomarker was utilized to diagnose the response variable.