International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 07 | July 2024
p-ISSN: 2395-0072
www.irjet.net
Developing a Federated Learning-Based System for Personalized MentalHealth Assessment and Prediction Shrutika Malve*, Mahesh Vaswani*, Abhay Shanbhag*, Prof. Sarang Joshi SCTR’s Pune Institute of Computer Technology --------------------------------------------------------------------***---------------------------------------------------------------------
Abstract
predictive models which will be an extension to work in Korkmaz et al., 2022
In mental health care, depression presents a significant challenge, especially given the sensitivities around personal health data and the need for tailored therapies. This project introduces an innovative Federated Machine Learning (FML) framework designed specifically to analyze and predict depression while preserving data privacy and enhancing model accuracy.
Conventional methods employed in depression analysis Prabhudesai et al., 2021 , Minkowski et al., 2021 confront a dual dilemma: the imperative to uphold patient data confidentiality and the necessity to ensure the of datasets. Centralized data collection, though comprehensive, poses substantial privacy risks, potentially deterring participation, particularly within the sensitive realm of mental health care. Moreover, such centralized approaches as given by Zhang, 2022 often fall short in accommodating the di- verse manifestations of depression across various demographics, leading to less effective predictive models and interventions. The pro- posed FML framework addresses these intricate challenges by empowering local data processing on individual users’ devices, a strategic move aimed at safeguarding personal information. This methodology ensures that sensitive data remains within the user’s device, markedly enhancing privacy. Beyond its privacy-centric advantages, this approach allows for the assimilation of diverse and heterogeneous data, culminating in a model that is both inclusive and representative of the myriad manifestations of depression. The overarching objective of this project is to craft a robust, scalable model proficient in accurately predicting depression, offering insights crucial for informing personalized treatment strategies. Leveraging the capabilities of FML, the project aspires to establish a new standard in mental health care analytics
Traditional depression analysis methods strug- gle with maintaining patient confidentiality and ensuring diverse datasets. Centralized data col- lection, while thorough, poses substantial privacy risks and can deter participation, particularly in mental health contexts. Moreover, these methods often fail to capture the diverse manifestations of depression across different demographics, leading to less effective predictive models and interventions. The FML framework addresses these concerns by processing data locally on individual users’ devices, safeguarding personal information and ensuring that sensitive data remains on the user’s device. This approach enhances privacy and incorporates diverse and heterogeneous data, resulting in a model that is inclusive and representative of various depression manifestations. The primary goal is to create a robust, scalable model capable of accurately predicting depression and providing insights for personalized treatment strategies. By leveraging FML, the project aims to set a new standard in mental health care analytics, offering a privacy-conscious, scalable solution that can handle diverse data sources. This approach has the potential to revolutionize mental health care, providing a deeper under- standing of depression and paving the way formore effective, personalized care solutions.
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– A solution characterized by privacy conscious- ness, scalability, and adaptability to diverse data sources. This innovative approach holds the potential to revolutionize the landscape of mental health care, promising a more nuanced understanding of depression and heralding a new era of effective, personalized care solutions. Lai et al., 2021
Introduction
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Within the domain of mental health care, the formidable challenge of addressing depression is magnified by the intricate interplay of personal health data sensitivity and the demand for tailored therapeutic strategies. This re- search project, titled "Developing a Federated Learning-Based System for Personalized Mental Health Assessment and Prediction," introduces a groundbreaking initiative – a Federated Machine Learning (FML) framework meticulously crafted for the analysis and prediction of depression. The primary focus is on fortifying data privacy and refining the precision of
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Impact Factor value: 8.226
Related Work
2.1 Federated Machine Learning in Mental Health Analysis In Konečný et al., 2017 insights into the fundamentals of federated learning, emphasizing strategies to enhance communication efficiency in distributed machine learning. It lays a foundation for understanding how federated learning principles can be applied to mental health analysis.
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