Machine learning is transforming healthcare, and TensorFlow provides powerful tools for building AI models that can assist in medical diagnosis and patient care.
Setting Up Your Environment
Installing Required Dependencies
<br />
import tensorflow as tf<br />
import pandas as pd<br />
import numpy as np<br />
from sklearn.model_selection import train_test_split</p>
<p># Check TensorFlow version<br />
print(f"TensorFlow version: {tf.__version__}")</p>
<p># Load healthcare dataset<br />
data = pd.read_csv('patient_data.csv')<br />
print(data.head())<br />
Building a Simple Classification Model
Creating a Neural Network for Health Predictions
<br />
model = tf.keras.Sequential([<br />
tf.keras.layers.Dense(128, activation='relu', input_shape=(10,)),<br />
tf.keras.layers.Dropout(0.2),<br />
tf.keras.layers.Dense(64, activation='relu'),<br />
tf.keras.layers.Dense(1, activation='sigmoid')<br />
])</p>
<p>model.compile(optimizer='adam',<br />
loss='binary_crossentropy',<br />
metrics=['accuracy'])</p>
<p># Train the model<br />
history = model.fit(X_train, y_train,<br />
epochs=50,<br />
validation_data=(X_val, y_val))<br />
This approach has shown promising results in our healthcare AI projects.
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DevSecOps Engineer, AI/ML enthusiast, and technology blogger.
