An interpretable machine learning model for diagnosis of Alzheimer's disease

We present an interpretable machine learning model for medical diagnosis called sparse high-order interaction model with rejection option (SHIMR). A decision tree explains to a patient the diagnosis with a long rule (i.e., conjunction of many intervals), while SHIMR employs a weighted sum of short r...

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Bibliographic Details
Published in:PeerJ (San Francisco, CA) Vol. 7; p. e6543
Main Authors: Das, Diptesh, Ito, Junichi, Kadowaki, Tadashi, Tsuda, Koji
Format: Journal Article
Language:English
Published: United States PeerJ. Ltd 01.03.2019
PeerJ, Inc
PeerJ Inc
Subjects:
Accuracy
Algorithms
Alzheimer's disease
Alzheimer’s disease (AD)
Artificial intelligence
Bioinformatics
Biomarkers
Blood proteins
Cerebrospinal fluid
Cognitive Disorders
Computational Biology
Computer-aided diagnosis (CAD) model
Data Mining and Machine Learning
Decision making
Dementia
Diagnosis
Diagnostic imaging
Gene expression
General Data Protection Regulation
Interpretable model
Learning algorithms
Machine learning
Medical imaging
Neural networks
Neurodegenerative diseases
Neuroimaging
Neuroscience
NMR
Nuclear magnetic resonance
Patients
Physicians
Plasma proteins
Precision medicine
Proteins
Proteomics
SHIMR
Sparse high-order interaction
Japan
ADNI
Interpretable model
Alzheimer’s disease (AD)
Machine learning model
Sparse high-order interaction
Cost-effective framework
SHIMR
Classification with rejection option
Computer-aided diagnosis (CAD) model
Dementia
ISSN:2167-8359, 2167-8359
Online Access:Get full text
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Summary:We present an interpretable machine learning model for medical diagnosis called sparse high-order interaction model with rejection option (SHIMR). A decision tree explains to a patient the diagnosis with a long rule (i.e., conjunction of many intervals), while SHIMR employs a weighted sum of short rules. Using proteomics data of 151 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, SHIMR is shown to be as accurate as other non-interpretable methods (Sensitivity, SN = 0.84 ± 0.1, Specificity, SP = 0.69 ± 0.15 and Area Under the Curve, AUC = 0.86 ± 0.09). For clinical usage, SHIMR has a function to abstain from making any diagnosis when it is not confident enough, so that a medical doctor can choose more accurate but invasive and/or more costly pathologies. The incorporation of a rejection option complements SHIMR in designing a multistage cost-effective diagnosis framework. Using a baseline concentration of cerebrospinal fluid (CSF) and plasma proteins from a common cohort of 141 subjects, SHIMR is shown to be effective in designing a patient-specific cost-effective Alzheimer’s disease (AD) pathology. Thus, interpretability, reliability and having the potential to design a patient-specific multistage cost-effective diagnosis framework can make SHIMR serve as an indispensable tool in the era of precision medicine that can cater to the demand of both doctors and patients, and reduce the overwhelming financial burden of medical diagnosis.
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ISSN:2167-8359
2167-8359
DOI:10.7717/peerj.6543