S13 Data - by Shehr Bano (20421706)

S19 Data - by Shehr Bano (20421706)

S3 Data - by Shehr Bano (20421706)

S7 Data - by Shehr Bano (20421706)

S9 Data - by Shehr Bano (20421706)

S15 Data - by Shehr Bano (20421706)

S18 Data - by Shehr Bano (20421706)

S20 Data - by Shehr Bano (20421706)

S1 Data - by Shehr Bano (20421706)

S14 Data - by Shehr Bano (20421706)

The structure of the NANEAT model corresponds to the optimal species genome. by Wenbing Shi (5806160)

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Evaluation indexes of different methods on the test set. by Wenbing Shi (5806160)

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The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

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The structure of the ANEAT model corresponds to the optimal species genome. by Wenbing Shi (5806160)

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The construction process of the CGO prediction model. by Wenbing Shi (5806160)

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The comparison of different prediction methods on the test set. by Wenbing Shi (5806160)

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Supplementary file 1_Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registers.docx by Toni Mora (8145624)

“…</p>Objective<p>To develop and evaluate explainable machine learning models that predict the risk of anaphylaxis using routinely collected clinical data.…”

Speed efficiency of the proposed technique in comparison with the baseline deep learning models. by Amin Ullah (12015113)

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Proposed Federated Learning Framework. by Mohsen H. Alhazmi (22756263)

“…<div><p>Federated learning (FL) struggles with scalability in decentralized big data platforms due to data heterogeneity, communication bottlenecks, and computational inefficiencies. …”

External cohort raw data. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”