BioMarkML: Machine Learning for Early Diagnosis of Alzheimer Disease
Research type
Research Study
Full title
BioMarkML: Machine Learning-Driven Bio-Sensing for Early Diagnosis of Alzheimer’s Disease
IRAS ID
352434
Contact name
Muhammad Hamza Rafique Bhatti
Contact email
Sponsor organisation
University of Plymouth
Duration of Study in the UK
3 years, 11 months, 30 days
Research summary
In this PhD project we will develop machine learning (ML) models to enable classification of case vs control data sets for protein AD biomarkers using multiplexed graphene sensors that have already been demonstrated to have ultra-high sensitivity and specificity. The ML stage will involve; (i) Feature Selection: pre-processing bio-signals (conductance & impedance) for analysis, employing techniques to iteratively remove the least important features and prevent model overfitting, and utilizing ML models to assess and rank feature importance; (ii) Pattern Discovery: through clustering approaches to understand the underlying patterns of bio-signals and to inform the classification process; (iii) Classification:to differentiate Alzheimer's patients and healthy controls; (iv) Validation: to rigorously evaluate the classification model's performance and ensure its reliability and generalizability, and (v) Optimisation: tuning hyper-parameters or refining the data preprocessing to optimize the model’s performance.
REC name
South West - Central Bristol Research Ethics Committee
REC reference
25/SW/0066
Date of REC Opinion
31 Jul 2025
REC opinion
Further Information Favourable Opinion