Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease.
Diagnosis of Alzheimer's disease (AD) is often difficult, especially early in the disease process at the stage of mild cognitive impairment (MCI). Yet, it is at this stage that treatment is most likely to be effective, so there would be great advantages in improving the diagnosis process. We describe and test a machine learning approach for personalized and cost-effective diagnosis of AD. It uses locally weighted learning to tailor a classifier model to each patient and computes the sequence of biomarkers most informative or cost-effective to diagnose patients. Using ADNI data, we classified AD versus controls and MCI patients who progressed to AD within a year, against those who did not. The approach performed similarly to considering all data at once, while significantly reducing the number (and cost) of the biomarkers needed to achieve a confident diagnosis for each patient. Thus, it may contribute to a personalized and effective detection of AD, and may prove useful in clinical settings.
Signal Processing and Multimedia Communications Research Group, School of Computing and Mathematics, Plymouth University, Plymouth, PL4 8AA, UK. javier.escudero@ieee.org
Javier
J
Escudero
Emmanuel
E
Ifeachor
John P
JP
Zajicek
Colin
C
Green
James
J
Shearer
Stephen
S
Pearson
D000544:Alzheimer Disease / Q000097:blood / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D001185:Artificial Intelligence; D015415:Biomarkers / Q000097:blood / Q000378:metabolism; D060825:Cognitive Dysfunction / Q000097:blood / Q000175:diagnosis / Q000378:metabolism / Q000473:pathology; D003362:Cost-Benefit Analysis; D016208:Databases, Factual; D018450:Disease Progression; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D057285:Precision Medicine / Q000191:economics / Q000379:methods*; D015203:Reproducibility of Results
D015415:Biomarkers