Citation

Shubar, Abduelhakem and Ramakrishnan, Kannan and Ho, Chin Kuan (2022) Machine Learning Models for Early Prediction and Management of Dementia Related Disorders. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

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Abstract

More than 55 million people diagnosed with dementia around the world in 2020. Dementia care costs over US$ 1.3 trillion annually world wide. It is anticipated to increase to US$ 2.8 trillion by 2030. 1 • A report reveals that majority of dementia cases between 2010 and 2050 reside in low and middle income countries. 2 • Majority of research work in regards to developing dementia prediction solutions are based on invasive and costly methods. • A model based on structural and non-structural EMR data achieved accuracy of 80%. 3 • Authors trained a minimal recurrent neural network (RNN) model to predict AD up to 6 years with an average 88.7% accuracy using one to 4 timesteps of multimodal AD and clinical diagnosis. 4 • A support vector machine model for dementia prediction was trained over a data set of personal medical history features. The model achieved F-score of 80.9%. 5 • A gradient boosting machine model was trained with administrative and EHR data to predict onset of dementia from 3 to 8 year.6

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	Machine Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	20 Dec 2022 03:34
Last Modified:	20 Dec 2022 03:34
URII:	http://shdl.mmu.edu.my/id/eprint/10949

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