Multimodal protective and susceptibility clusters in paediatric atopic dermatitis: A machine learning-based, data-driven observational study.

This project uses advanced machine learning to find groups of children with atopic dermatitis who are either protected or at risk, using data from many sources. Because childhood eczema varies a lot, usual clinical grouping often misses the complex, non‑linear links among genes, environment, lifestyle, and medical factors. To fix this, our framework applies unsupervised machine‑learning methods—such as multi‑view consensus clustering and deep clustering—to combine different kinds of data. We start by cleaning the multimodal clinical records, handling any missing values, and applying strong dimensionality‑reduction techniques before running the clustering analysis. By uncovering clear patient phenotypes, the system aims to separate groups that have protective factors from those likely to

develop severe disease. We evaluate the results with metrics like the silhouette coefficient, the Davies‑Bouldin index, and other clinical validation proxies to make sure the clusters are stable and biologically meaningful. The project also offers full implementation help, guidance on structuring the work, and evaluation methods for M.Tech students who want to build data‑driven observational models in clinical informatics.

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