# I Trained a Neural Network on Real Medical Data and Fit It in 8.9 kB of Pure C > Source: > Published: 2026-06-20 04:16:08+00:00 I want to be honest with you from the start: I didn't set out to write this article. I set out to answer a question I had about my own tool — *can Hasaki do regression?* — and somewhere between loading a dataset of 3,658 real patient records and watching a microcontroller predict blood pressure from 14 numbers, I realized the answer deserved to be written down. Hasaki is a command-line neural network trainer I built for embedded systems developers. You train a model on your desktop, and it exports a self-contained C header — weights, biases, and a `predict()` function — ready to drop into any MCU project. No TensorFlow. No runtime. No dependencies. Every public demo I've done has been classification: smoke detection, MNIST digits, motion sensing. Binary or multiclass outputs. But Hasaki has a `linear` activation. And `sigmoid` . And a single-neuron output layer. The pieces for regression were always there. I just never tested them. So I decided to test them properly — with real data, a real target, and enough honesty to document what broke along the way. I wanted something biomédical. Not because I'm building a medical device — I'm not, and I'll be clear about that — but because medical data carries weight. It's messy, it's human, and if the numbers are wrong, they mean something. The Framingham Heart Study dataset on Kaggle fit the criteria: 4,240 patient records with 15 attributes — age, cholesterol, BMI, glucose, smoking habits, blood pressure — collected over decades from residents of Framingham, Massachusetts. The target most people use is `TenYearCHD` , a binary classification of 10-year heart disease risk. I ignored that. I went after `sysBP` — systolic blood pressure — a continuous value ranging from 83.5 to 295.0 mmHg. That's a regression problem. Before Hasaki could see a single number, I had to clean the data. 645 rows had missing values — about 15% of the dataset. I dropped them. That left 3,658 samples, which is workable. Then I had to normalize everything. Hasaki expects inputs in a consistent range. Raw features don't come that way: age runs 32–70, cholesterol 107–696, glucose 40–394. I used `RobustScaler` from scikit-learn — more resistant to outliers than `MinMaxScaler` , which matters when you have 3,658 people's real health data. The target needed normalization too. With `sigmoid` as the output activation, predictions are bounded to `[0, 1]` . So I normalized `sysBP` to that range: ``` y_scaled = (target - target_min) / (target_max - target_min) # target_min = 83.5, target_max = 295.0 ``` This is the part that frustrates me about my own tool. A new user shouldn't need to know any of this. They should hand Hasaki a CSV and get a model. That's a v4.0.0 problem — and it's already on the roadmap. Architecture: `14,32,16,1` — fourteen inputs, two hidden layers, one output. ``` hasaki -d 14,32,16,1 -act relu,relu,sigmoid -a train \ -f framingham_hasaki.csv -e 10000 -l 0.001 --adam \ -o bp_model.txt ``` It trained in 23 seconds. Early stopping triggered at epoch 218. **Final Val Loss: 0.002420** That number lives in normalized space. To make it mean something: ``` RMSE = sqrt(0.002420) × (295.0 - 83.5) ≈ 10.4 mmHg ``` The mean absolute error across the validation set was approximately **7.8 mmHg**. For a single sample — a male patient, age 39, non-smoker, no diabetes, cholesterol 195, BMI 26.97 — the model predicted **107.4 mmHg**. The real value was **106.0 mmHg**. Error: **1.4 mmHg**. Clinical-grade blood pressure monitors are rated at ±3 mmHg. There's something I need to flag for anyone using Hasaki for regression: the training log shows `Train Acc` and `Val Acc` alongside the loss values. During my regression run, both read `1.0000` from the very first epoch. That's not because the model is perfect. It's because the accuracy metric uses a 0.5 threshold designed for binary classification — and in regression, nearly every prediction clears that bar trivially. The accuracy columns are meaningless for regression. Ignore them. Watch Val Loss. This is a known issue and it's going in the fix list for v4.0.0. After validating, I ran the INT8 quantization test: ``` Result: PASSED Mean error (float): 0.037270 Mean error (INT8): 0.037251 ``` Practically identical. I exported: ``` hasaki -m bp_model.txt -a export -o bp_model.h -q int8 ``` **File size: 8,893 bytes — 8.9 kB.** A model that predicts systolic blood pressure from 14 patient vitals, quantized to INT8, fits in 8.9 kB of flash. No runtime. No interpreter. No Python at inference time. Just a C header and a `predict()` call. ``` #include "bp_model.h" float input[14] = { /* normalized vitals */ }; float output[1]; predict(input, output); float systolic_bp = output[0] * (295.0f - 83.5f) + 83.5f; ``` I built Hasaki for classification. I documented it for classification. Every demo I've published has been classification. Regression was never a stated feature. It was a consequence of having `linear` and `sigmoid` activations and a single output neuron — the right pieces in the right place. I only found out it worked by testing it. That surprised me more than the 1.4 mmHg prediction. Not because the math is surprising — a neural network doesn't care if the target is a class label or a blood pressure reading. But because the tool had a capability I didn't know it had until I used it outside its intended purpose. There's something worth sitting with there. We tend to define our tools by the problems we built them for. But a good tool, used honestly, sometimes reveals what it can do before we think to ask. I didn't know Hasaki could do regression. Now it's in the manual. This is a research experiment, not a medical device. The model was trained on the Framingham Heart Study dataset for demonstration purposes only. Do not use Hasaki-trained models for clinical diagnosis without independent validation by qualified medical professionals. *Hasaki 刃先 — Small tools. Sharp inference.* [Hasaki 刃先 Free](https://github.com/AlexRosito67/hasaki) · [Hasaki 刃先 Pro](https://hasaki.lemonsqueezy.com)