Поскольку COVID-19 продолжает бушевать, существует постоянная потребность в применении машинного обучения в здравоохранении. Здоровье — важная часть нашей жизни, которую можно значительно улучшить с помощью машинного обучения. Некоторые области применения в области здравоохранения включают в себя;
- Диагностика заболеваний
2. Открытие и производство лекарств
3. Индивидуальное лечение
4. Умная запись здоровья
5. Прогнозирование и сдерживание вспышки
и много других.
Некоторое время назад я построил модель, которая предсказывает диабет, используя набор данных с открытым исходным кодом, и поэтому решил создать веб-приложение на основе этой модели. Веб-приложение будет учитывать возраст пользователя, уровень глюкозы и ИМТ, чтобы предсказать возможность диагноза диабета. Обратите внимание, что это не предназначено для профессионального медицинского диагноза, но, безусловно, может быть улучшено для этого. Это только для целей обучения.
Настраивать
Давайте начнем наше приложение с создания виртуальной среды. Я буду использовать conda от Anaconda, так как его легко настроить. Запустите следующую команду на своем терминале, если у вас установлены conda или anaconda.
# create env conda create --name diabetes # activate it conda activate diabetes
Затем установите зависимости из файла requirements.txt:
pip install -r requirements.text
app.py
Далее мы создадим наше приложение. Структура папок нашего проекта будет выглядеть следующим образом:
.
├── app.py
├── diabetes.sav
├── README.md
├── requirements.txt
└── template
└── index.html
Вы поймете, что я также добавил файл diabetes.sav, который является моей сохраненной моделью из scikit-learn. Этот файл и код для этого проекта можно найти здесь.
Код для файла app.py будет выглядеть следующим образом:
import pickle
from fastapi import FastAPI
from starlette.requests import Request
from fastapi.templating import Jinja2Templates
import numpy as np
app = FastAPI()
templates = Jinja2Templates(directory="template")
# the filename of the saved model
filename = 'diabetes.sav'
# load the saved model
loaded_model = pickle.load(open(filename, 'rb'))
@app.get("/")
def home(request: Request):
return templates.TemplateResponse("index.html", {"request": request})
@app.post('/predict')
async def predict(request: Request):
result = {}
if request.method == "POST":
# get the features to predict
form = await request.form()
# form data
age = form["Age"]
glucose = form["Glucose"]
bmi = form["BMI"]
# create the features list for prediction
features_list = [int(glucose), int(bmi), int(age)]
# get the prediction class
prediction = loaded_model.predict([features_list])
# get the prediction probabilities
confidence = loaded_model.predict_proba([features_list])
# formulate the response to return to client
result['prediction'] = int(prediction[0])
result['confidence'] = str(round(np.amax(confidence[0]) * 100 ,2))
return templates.TemplateResponse("index.html", {"request": request, "result": result})
В приведенном выше файле мы сначала импортируем наши библиотеки, а затем создаем экземпляр FasAPI, веб-фреймворка, который мы используем для создания нашего приложения. Затем мы создаем переменную templates, которая указывает на наш файл шаблона. После этого мы создаем переменную (filename), которая указывает на наш сохраненный файл модели.
Затем мы создаем два маршрута; маршрут get, который служит нашим домашним маршрутом, а затем отображает нашу форму, которая будет принимать данные пользователя. Маршрут post — это место, где происходит обработка нашей формы. В этом маршруте мы сначала создаем пустой словарь, который будет содержать наш прогноз. Затем мы убеждаемся, что наш метод запроса post требуется для данных формы. Затем мы извлекаем входные данные в список, который можем передать нашей модели, вызвав для нее метод .predict. Мы также получаем вероятность предсказания, используя метод .predict_prob.
Наконец, мы визуализируем результат в нашем файле index.html.
index.html
Этот файл будет выглядеть следующим образом:
<!DOCTYPE html>
<html>
<head>
<!--Import Google Icon Font-->
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<!-- Compiled and minified CSS -->
<link
href="https://unpkg.com/tailwindcss@^2/dist/tailwind.min.css"
rel="stylesheet"
/>
<title>Diabetes Predictor</title>
<style>
body {
background-color: #dfdbe5;
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display: flex;
height: 100vh;
justify-content: center;
align-items: center;
}
</style>
</head>
<body>
<section class="ml-auto text-gray-700 body-font">
<div class="container flex flex-wrap items-center px-5 py-24 mx-auto">
<div class="pr-0 md:w-1/2 md:pr-16 lg:pr-0">
<h1
class="mt-24 text-5xl font-medium font-bold text-gray-600 lg:mt-0"
>
Diabetes Predictor
</h1>
<p class="mt-4 text-xl font-medium leading-relaxed text-gray-600">
Predict the likelihood of being diagnosed
with Diabetes by providing
your Age, BMI and Glucose level
</p>
<div class="mt-10 mr-4">
{% if result %}
<div class="">
{% if result.prediction == 1 %}
<p class="text-5xl font-bold text-gray-500">
You are <span class="text-indigo-500">Diabetic</span>
</p>
{% else %}
<p class="text-5xl font-bold text-gray-500">
You are <span class="text-pink-500">Not Diabetic</span>
</p>
{% endif %}
<p class="mt-4 text-lg font-bold text-gray-600">
The confidence Level is <span class="text-pink-500">{{result.confidence}}% </span>
</p>
</div>
{% endif %}
</div>
</div>
<form method="post" action="/predict">
<div
class="flex flex-col w-full p-8 mt-10 text-xl bg-gray-800 rounded-lg lg:w-5/6 md:w-1/2 md:ml-auto md:mt-0"
>
<div class="relative mb-4">
<label for="Age" class="text-sm font-bold leading-7 text-gray-100"
>Age(years)</label
>
<input
type="number"
id="Age"
name="Age"
required
class="w-full px-3 py-1 text-base leading-8 text-gray-700 transition-colors duration-200 ease-in-out bg-white border border-gray-300 rounded outline-none focus:border-indigo-500"
/>
</div>
<div class="relative mb-4">
<label
for="glucose"
class="text-sm font-bold leading-7 text-gray-100"
>Glucose Level(mg/dL)</label
>
<input
type="number"
id="Glucose"
name="Glucose"
required
class="w-full px-3 py-1 text-base leading-8 text-gray-700 transition-colors duration-200 ease-in-out bg-white border border-gray-300 rounded outline-none focus:border-indigo-500"
/>
</div>
<div class="relative mb-4">
<label for="BMI" class="text-sm font-bold leading-7 text-gray-100"
>BMI(lbs)</label
>
<input
type="number"
id="BMI"
name="BMI"
required
class="w-full px-3 py-1 text-base leading-8 text-gray-700 transition-colors duration-200 ease-in-out bg-white border border-gray-300 rounded outline-none focus:border-indigo-500"
/>
</div>
<input
class="px-8 py-2 text-lg text-white bg-pink-500 border-0 rounded focus:outline-none hover:bg-pink-600"
type="submit"
value="Predict"
/>
</div>
</form>
<br />
</div>
</section>
<script src="https://cdn.jsdelivr.net/npm/sweetalert2@10"></script>
<script>
Swal.fire(
"Please Note!",
"This is for learning purposes and is not meant to be a qualified medical diagnosis. The model is not super accurate!",
"error"
);
</script>
</body>
</html>
Мы используем Tailwindcss; крутая библиотека CSS, ориентированная на утилиты, которая позволяет быстро создавать веб-сайты, и мы также используем Heropatterns для классного фона.
Наша логика в шаблоне проста. Сначала мы проверяем, есть ли результат, а затем проверяем, равен ли прогноз результата 1. Если он равен 1, мы говорим, что пользователь диабетик. В противном случае его нет. Мы используем шаблонизатор Jinja2.
В приведенной ниже строке используется SweetAlert2 для отображения отказа от ответственности в отношении приложения.
Swal.fire(
"Please Note!",
"This is for learning purposes and is not meant to be a qualified medical diagnosis. The model is not super accurate!",
"error"
);
Давайте, наконец, запустим приложение, добавив следующее в app.py
# app.py
# import uvicorn at the top of app.py
import uvicorn
# then add the following to the bottom of app.py
if __name__ == "__main__":
uvicorn.run("app:app", host="127.0.0.1", port=8000, reload=True)
Затем, наконец, запустите приложение с помощью:
python app.py
Если все пойдет хорошо, вы должны увидеть следующее;
Вывод
Машинное обучение пришло, чтобы остаться и сегодня приносит нам огромные преимущества. Это может значительно сэкономить время, необходимое для диагностики таких простых заболеваний, как диабет. Это приложение только что показало вам, как это сделать. Есть так много других областей здравоохранения, в которых мы можем применять машинное обучение. Надеюсь, вы вдохновились на создание своего мл-приложения.
