Please use this identifier to cite or link to this item: http://localhost:80/xmlui/handle/123456789/13055
Title: Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI)
Authors: Iftikhar, Sadaf
Shahid, Saman
Umar Hassan, Muhammad
Ghias, Mamoona
Keywords: Restless leg syndrome
multilayer perceptron
diabetes mellitus
sleep disturbance
urge to move
Issue Date: 4-Sep-2020
Publisher: Karachi:Pakistan Journal of Pharmaceutical Sciences, university of Karachi.
Citation: Iftikhar, S., Shahid, S., Hassan, M. U., & Ghias, M. (2020). Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI). Pakistan Journal of Pharmaceutical Sciences, 33.
Abstract: This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes mellitus (DM) type-2, thorough artificial intelligence based multilayer perceptron (MLP). 300 cases of diabetes mellitus type-2, of age between 18-80 years were included. Point-biserial correlation/Pearson Chi-Square correlations were conducted between RLS and risk factors. We trained a backpropagation MLP via. supervised learning algorithm to predict clinical outcome for RLS. Majority of the patients were having hypertension (63%) and with peripheral neuropathy (69%). Two mostly reported scaled parameters were: 18% ‘tiredness’ and 14%, ‘impact on mood’. A significant correlation was found in RLS with smoking, hypertension and chronic renal failure (CRF). MLP model achieved more than 95% accuracy in predicting the outcome with cross entropy error 0.5%. Following scaled symptomatic variables: ‘need/urge to move’ (100%) achieved the highest normalized importance, followed by ‘relief by moving’ (85.7%), ‘sleep disturbance’ (62%) and ‘impact on mood’ (51.3%). Artificial intelligence based models can help physicians to identify the pre diagnose RLS, so that active measures can be taken in time to avoid further complications.
URI: http://142.54.178.187:9060/xmlui/handle/123456789/13055
ISSN: 1011-601X
Appears in Collections:Issue 5

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