Programma Python che utilizza un algoritmo di apprendimento automatico per classificare i risultati della patologia come...

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load the dataset
data = pd.read_csv("pathology_results.csv")

# Split the data into features and labels
X = data.drop("cancerous", axis=1)
y = data["cancerous"]

# Split the data 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)

# Train a random forest classifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)

# Make predictions on the test set
y_pred = clf.predict(X_test)

# Print the accuracy of the model
print("Accuracy:", accuracy_score(y_test, y_pred))