23/08/2026
Building a neural network for patient diagnosis, in 9 steps 👇
1️⃣ Define the problem — disease / no disease, or which disease?
2️⃣ Collect data — demographics, labs, symptoms, imaging, history
3️⃣ Preprocess — missing values, encoding, normalization, 70/15/15 split
4️⃣ Explore — distributions, correlation matrix, outliers
5️⃣ Design — layers, neurons, activations, sigmoid or softmax output
6️⃣ Train — binary crossentropy, Adam, validate each epoch
7️⃣ Evaluate — precision, recall, F1, ROC-AUC, confusion matrix
8️⃣ Improve — tune hyperparameters, regularize, add data
9️⃣ Deploy — integrate, then monitor for drift
The steps everyone rushes: 3 and 7.
Bad preprocessing quietly poisons everything downstream. And in medicine, accuracy alone hides the failure that matters — the sick patient your model called healthy.
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