Predicting Depression Among Type-2 Diabetic Patients Using Federated Learning

Rabia Tehseen ()
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Rabia Tehseen: University of Central Punjab, Lahore, Pakistan, 53700

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1984-1994

Abstract: Depression being a common and dangerous mental health condition could have a significant impact on a person's quality of life. It may result in depressive and gloomy feelings along with a loss of interest in once-enjoyable activities. Depression is considered a leading global cause of impairment that affects people at various stages of age, ethnicities, and socioeconomic status. It may cause negative effects on a person’s physical and emotional well-being like reduced motivation, energy, and appetite. In this paper, we have presented a Federated Learning-based framework to predict depression in patients with type 2 diabetes. Type 2 diabetes frequently coexists with depression, which can hurt treatment outcomes and raise medical expenses. The objective of thispaper is to create a Federated Learning-based framework to predict the impact of depression in causing type-II diabetes by analyzing patient’s data including laboratory results, medical history, and demographic information. To forecast the likelihood of depression in patients with type 2 diabetes. Analysis has been performed using a freely available dataset of Type-II diabetes from Kaggle and an accuracy of 97% has been achieved.

Keywords: Depression; Federated Learning; Type 2 diabetes; Healthcare (search for similar items in EconPapers)
Date: 2024
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