Journal
PEERJ
Volume 7, Issue -, Pages -Publisher
PEERJ INC
DOI: 10.7717/peerj.6543
Keywords
Dementia; Interpretable model; Sparse high-order interaction; Alzheimer's disease (AD); Computer-aided diagnosis (CAD) model; SHIMR; ADNI; Cost-effective framework; Machine learning model; Classification with rejection option
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Funding
- Materials Research by Information Integration Initiative (MI2I) project
- Core Research for Evolutional Science and Technology (CREST) from the Japan Science and Technology Agency (JST) [JPMJCR1502]
- Ministry of Education, Culture, Sports, Science and Technology (MEXT) through Priority Issue on Post-K computer (Building Innovative Drug Discovery Infrastructure through Functional Control of Biomolecular Systems)
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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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