Multi-view feature selection and classification for Alzheimer’s Disease diagnosis

2016 ◽  
Vol 76 (8) ◽  
pp. 10761-10775 ◽  
Author(s):  
Mingxing Zhang ◽  
Yang Yang ◽  
Fumin Shen ◽  
Hanwang Zhang ◽  
Yuan Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Disease Diagnosis ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Erratum to: Multi-view feature selection and classification for Alzheimer’s Disease Diagnosis

2016 ◽  
Vol 76 (8) ◽  
pp. 10777-10778
Author(s):  
Mingxing Zhang ◽  
Yang Yang ◽  
Fumin Shen ◽  
Hanwang Zhang ◽  
Yuan Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Disease Diagnosis ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach

2015 ◽  
Vol 30 (1) ◽  
pp. 43-60 ◽  
Author(s):  
Karmele López-de-Ipiña ◽  
Jordi Solé-Casals ◽  
Harkaitz Eguiraun ◽  
J.B. Alonso ◽  
C.M. Travieso ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Fractal Dimension ◽  
Spontaneous Speech ◽  
Disease Diagnosis ◽  
Speech Analysis ◽  
Selection For ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Accuracy-time optimisation with cost-sensitive feature selection for Alzheimer's disease diagnosis

2021 ◽  
Author(s):  
Niamh McCombe ◽  
Xuemei Ding ◽  
Girijesh Prasad ◽  
Paddy Gillespie ◽  
David P Finn ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Diagnostic Accuracy ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Assessment Time ◽  
Alzheimer’S Disease Diagnosis ◽  
The Cost ◽  
The Impact

Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the cost of assessment items, in terms of assessment time, has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer's disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open source dementia dataset. We use cost cost-sensitive feature selection to optimise our feature selection results for assessment time as well as accuracy. To encourage the clinical adoption and further evaluation of our proposed accuracy-vs-time optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-time optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, the cost-benefit optimisation analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.

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