CancerDetectionModel.py

import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf

# Load and prepare the dataset
dataset = pd.read_csv('CancerStats.csv')

# Separate features and labels
x = dataset.drop(columns=["diagnosis(1=m, 0=b)"])
y = dataset["diagnosis(1=m, 0=b)"]

# Split the dataset into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# Define the model
model = tf.keras.models.Sequential()

# Correct the input shape in the first layer
model.add(tf.keras.layers.Dense(256, input_shape=(x_train.shape[1],), activation='sigmoid'))
model.add(tf.keras.layers.Dense(256, activation='sigmoid'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(x_train, y_train, epochs=100, validation_split=0.2)

# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print(f'Test Loss: {loss}')
print(f'Test Accuracy: {accuracy}')