# Health Analysis with LatticaAI Demo Tutorial

<div data-full-width="true"><figure><img src="/files/1mE2nSZeM9sdmRH5eFTw" alt=""><figcaption></figcaption></figure></div>

## Overview of the Model

Our **Health Analysis** model is trained on the [Disease Prediction Kaggle dataset](https://www.kaggle.com/datasets/kaushil268/disease-prediction-using-machine-learning). This dataset is designed to facilitate the application of machine learning to the medical field, aiding physicians by automating disease diagnosis based on symptoms.

The dataset consists of **131 binary columns** representing different symptoms that a person may experience, and maps symptoms to **41 different diseases**, allowing classification based on input symptoms.

We trained **multi-class logistic regression** model,  implemented the equivalent fully homomorphic model and deployed it to our cloud service for secure inference.

* **Input Format**: binary vector of length `131`.
* **Output**: Probability vector of length `41` that represents the possible diseases.

The equivalent pytorch code for the inference is:

{% file src="/files/rkaIBxNRlGoMeTJ5KphB" %}
List of 131 possible symptoms
{% endfile %}

{% file src="/files/GJEIvjLSwlbmrSFalC35" %}
List of 41 disease
{% endfile %}

{% file src="/files/9AxirP5Ko9xVwnp2DiQN" %}

{% file src="/files/vBKaPxf6OqEXooYVKBhq" %}

```python
import torch
import json
import numpy as np


# load the symptoms and diseases mappings
with open('symptoms.json', 'r') as f:
    symptoms = json.load(f)

with open('diseases.json', 'r') as f:
    diseases = json.load(f)

# load the coef matrix and intercept vector of the trained logistic regression model
W = np.load('coef.npy')  # shape (41,131)
b = np.load('intercept.npy')  # shape (41,)

# create binary vector of length 131, corresponding to the mapping symptoms.json
pt = torch.zeros(131, dtype=torch.float64)
pt[symptoms['Continuous Sneezing']] = 1
pt[symptoms['Shivering']] = 1
pt[symptoms['Chills']] = 1
pt[symptoms['Watering From Eyes']] = 1

# calculate the logistic regression prediction
res = torch.nn.functional.softmax(pt @ W.T + b, dim=0)

# the predicted disease according to the mapping diseases.json
disease = diseases[res.argmax().item()]
print(f"Predicted disease: {disease}")
```

## Achieving Full Privacy with LatticaAI

{% hint style="info" %}
First [install](/demo-tutorials/zooming-into-each-step-of-demo-run-with-latticaai-flow.md#installation-and-setup) our client package
{% endhint %}

{% tabs %}
{% tab title="🧊 Python SDK" %}

```python
from lattica_query.auth import get_demo_token
from lattica_query.lattica_query_client import QueryClient

model_id = "healthPrediction"
my_token = get_demo_token(model_id)

client = QueryClient(my_token)

context, secret_key, client_blocks, = client.generate_key()

# `pt` and `res` are torch.Tensor, same as in the plain example above
pt = torch.randint(0, 2, (131,), dtype=torch.float64)

res = client.run_query(context, secret_key, pt, client_blocks)
```

{% endtab %}

{% tab title="📦 TypeScript SDK" %}

```typescript
import { getDemoToken, LatticaQueryClient } from '@Lattica-ai/lattica-query-client';

const modelId = "healthPrediction"
const token = await getDemoToken(modelId);

const client = new LatticaQueryClient(myToken);

await client.init();

const pt = [1, 1, 0, , 0, 1,...];
const result = await client.runQuery(pt);
```

{% endtab %}
{% endtabs %}

{% hint style="info" %}
See our [step-by-step guide](/demo-tutorials/zooming-into-each-step-of-demo-run-with-latticaai-flow.md) for a detailed explanation of each step in this flow. \
To use the image sharpening model use the  **healthPrediction** model ID
{% endhint %}


---

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```
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