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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Veröffentlicht in:	PeerJ (San Francisco, CA) Jg. 7; S. e6543
Hauptverfasser:	Das, Diptesh, Ito, Junichi, Kadowaki, Tadashi, Tsuda, Koji
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ. Ltd 01.03.2019 PeerJ, Inc PeerJ Inc
Schlagworte:	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-Zugang:	Volltext
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