Regional analysis of socioeconomic indicators and federated learning performance gain.

<p>The table presents a systematic comparison across Brazil’s five macro-regions, linking key socioeconomic and health indicators with the study’s sample characteristics (number of hospitals and average patient cohort size per hospital). The primary outcome, the mean performance gain from fede...

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מחבר ראשי:	Roberta Moreira Wichmann (14259316) (author)
מחברים אחרים:	Murilo Afonso Robiati Bigoto (22676715) (author), Alexandre Dias Porto Chiavegatto Filho (14259328) (author)
יצא לאור:	2025
נושאים:	Cell Biology Ecology Cancer Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified via parameter aggregation using ensemble aggregation strong inverse correlation maximum performance degradation benefits requires caution beneficial collaborative effect >&# 916 ;</ 19 mortality using 19 mortality prediction local patient volume statistically certain benefits statistical significance showed 0273 ]) due auc crossed zero study validates fl fl models demonstrated performance gain (< evaluated federated learning div >< p collaborative model ’ auc minus auc local data scarcity ci [– 0 gain ’ federated learning local validation universally certain statistical advantage 6307 ]) auc across – 0 volatile algorithm smallest hospital particularly evident network (< multicentric sample multicenter sample limited institutions layer perceptron iid ). high sensitivity findings underscore every site enabling mechanism despite achieving confidence intervals 500 ). 022 patients
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סיכום:	<p>The table presents a systematic comparison across Brazil’s five macro-regions, linking key socioeconomic and health indicators with the study’s sample characteristics (number of hospitals and average patient cohort size per hospital). The primary outcome, the mean performance gain from federated learning (<i>Δ</i>AUC = AUC - AUC), is presented for each of the three models: Logistic Regression (LR), Multilayer Perceptron (MLP), and Random Forest (RF). The mean <i>Δ</i>AUC values for each region were calculated by averaging the hospital-specific mean <i>Δ</i>AUCs reported in S1 Table (for Logistic Regression), <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013695#pcbi.1013695.s003" target="_blank">S2 Table</a> (for MLP), and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013695#pcbi.1013695.s004" target="_blank">S3 Table</a> (for Random Forest) across all hospitals within that geographical region. This allows for a higher-level investigation of the relationship between regional characteristics and the benefits of the federated approach.</p> <p>(XLSX)</p>

Regional analysis of socioeconomic indicators and federated learning performance gain.

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