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Medical Diagnosis Neural Network 2D

2D companion to the 3D medical diagnosis neural network: the same three-input, sigmoid-hidden, softmax-output feedforward network as a flat wiring diagram, with live probability bars and a rolling confidence readout.

AI & Machine Learning2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

This 2D companion runs the identical three-input feedforward network as the 3D version — age risk, biomarker level and symptom severity flow through two dense sigmoid hidden layers to a three-class softmax output (Healthy / At-risk / Disease) — but renders it as a flat wiring diagram built for reading the math: node brightness tracks activation, connection opacity tracks weighted signal strength, a live bar chart shows the three class probabilities against the decision-threshold slider, and a scrolling confidence strip shows how the winning prediction's certainty moves as you drag the sliders.

⚙ Under the hood

2D companion to the 3D medical diagnosis neural network: the identical three-input, two-hidden-layer feedforward network (age risk, biomarker level, symptom severity into sigmoid hidden layers, softmax output) drawn as a flat wiring diagram instead of a rotating 3D scene, with a live class-probability bar chart, a scrolling confidence-over-time strip, and the same decision-threshold slider.

neural networkfeedforwardsigmoidsoftmaxmedical diagnosisclassification2d-simulation

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install