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Authors: Riccardo Dondi 1 and Mehdi Hosseinzadeh 1 ; 2

Affiliations: 1 Universit` a degli Studi di Bergamo, Bergamo, Italy ; 2 University of Calabria, Rende(CS), Italy

Keyword(s): Network Analysis, Disability Classification, Learning Algorithms, Healthcare Analytics, Graph Data Mining.

Abstract: This contribution proposes a novel network analysis model with the goal of predicting a classification of individuals as either ‘disabled’ or ‘not-disabled’, using a dataset from the Health and Retirement Study (HRS). Our approach is based on selecting features that span health indicators and socioeconomic factors due to their pivotal roles in identifying disability. Considering the selected features, our approach computes similarities between individuals and uses this similarity to predict disability. We present a preliminary experimental eval-uation of our method on the HRS dataset, where it shows an enhanced average accuracy of 62.48%.

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Paper citation in several formats:
Dondi, R. and Hosseinzadeh, M. (2024). A Network Learning Method for Functional Disability Prediction from Health Data. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR; ISBN 978-989-758-716-0; ISSN 2184-3228, SciTePress, pages 358-362. DOI: 10.5220/0012991400003838

@conference{kdir24,
author={Riccardo Dondi. and Mehdi Hosseinzadeh.},
title={A Network Learning Method for Functional Disability Prediction from Health Data},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR},
year={2024},
pages={358-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012991400003838},
isbn={978-989-758-716-0},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR
TI - A Network Learning Method for Functional Disability Prediction from Health Data
SN - 978-989-758-716-0
IS - 2184-3228
AU - Dondi, R.
AU - Hosseinzadeh, M.
PY - 2024
SP - 358
EP - 362
DO - 10.5220/0012991400003838
PB - SciTePress