Machine Learning for the Preliminary Diagnosis of Dementia

Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.

[1]  L. McEvoy,et al.  Predicting MCI outcome with clinically available MRI and CSF biomarkers , 2011, Neurology.

[2]  Deepika Bansal,et al.  Comparative Analysis of Various Machine Learning Algorithms for Detecting Dementia , 2018 .

[3]  Nicola Amoroso,et al.  Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge , 2017, Journal of Neuroscience Methods.

[4]  Ming-Jang Chiu,et al.  A Nationwide Survey of Mild Cognitive Impairment and Dementia, Including Very Mild Dementia, in Taiwan , 2014, PloS one.

[5]  J. Morris.  The Clinical Dementia Rating (CDR) , 1993, Neurology.

[6]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[7]  Nicola Torelli,et al.  Training and assessing classification rules with imbalanced data , 2012, Data Mining and Knowledge Discovery.

[8]  Vladimir Fonov,et al.  Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.

[9]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Diane J. Cook,et al.  THIS SPACE MUST BE KEPT BLANK) Machine Learning Techniques for Diagnostic Differentiation of Mild Cognitive Impairment and Dementia , 2013, AAAI 2013.

[11]  Sabina Sonia Tangaro,et al.  Complex networks reveal early MRI markers of Parkinson's disease , 2018, Medical Image Anal..

[12]  E. Teng,et al.  The Cognitive Abilities Screening Instrument (CASI): A Practical Test for Cross-Cultural Epidemiological Studies of Dementia , 1994, International Psychogeriatrics.

[13]  Taghi M. Khoshgoftaar,et al.  Feature Selection with High-Dimensional Imbalanced Data , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[14]  Gerald Schaefer,et al.  Cost-sensitive decision tree ensembles for effective imbalanced classification , 2014, Appl. Soft Comput..

[15]  Haipeng Tang,et al.  NMD-12: A new machine-learning derived screening instrument to detect mild cognitive impairment and dementia , 2019, PloS one.

[16]  Yi-Chung Lee,et al.  Cutoff Scores of the Cognitive Abilities Screening Instrument, Chinese Version in Screening of Dementia , 2002, Dementia and Geriatric Cognitive Disorders.

[17]  M. Lawton,et al.  Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.

[18]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[19]  Teresa Wu,et al.  Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease , 2018, Translational research : the journal of laboratory and clinical medicine.