Temporal Change Analysis‐Based Recommender System for Alzheimer Disease Classification

Author(s):  
S. Naganandhini ◽  
P. Shanmugavadivu ◽  
M. Mary Shanthi Rani

The development of recommender systems gathered momentum due to its relevance and application in providing a personalized recommendation on a product or a service for customer relations management. It has proliferated into medicine and its allied domains for the recommendations on disease prediction/detection, medicine, treatment, and other medical services. This chapter describes a new composite and comprehensive recommender system named Temporal Change Analysis based Recommender System for Alzheimer Disease Classification (TCA-RS-AD) using a deep learning model. Its performance is evaluated on the dataset with T1-weighted MRI clinical temporal data of OASIS and the results were recorded in terms of Precision, Recall, F1-Score and Accuracy, Hamming Loss, Cohens Kappa Coefficient, and Matthews Correlation Coefficient. The improved accuracy of this recommendation model endorses its suitability for its application in the classification of AD


2017 ◽  
Vol 11 (10) ◽  
pp. 1169-1179 ◽  
Author(s):  
Tim R A van den Heuvel ◽  
Steven F G Jeuring ◽  
Maurice P Zeegers ◽  
Dorien H E van Dongen ◽  
Anouk Wolters ◽  
...  

2016 ◽  
Vol 39 ◽  
pp. 51-54
Author(s):  
Leonardo Disperati ◽  
Filippo Gregori ◽  
Massimo Perna ◽  
Francesco Manetti ◽  
Guido Lavorini ◽  
...  

2018 ◽  
Vol 29 ◽  
pp. 96-105 ◽  
Author(s):  
Xiuhua Song ◽  
Xinbo Lv ◽  
Dongming Yu ◽  
Qianqian Wu

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