91 lines
4.0 KiB
Python
91 lines
4.0 KiB
Python
|
import pandas as pd
|
||
|
|
from sklearn.model_selection import train_test_split
|
||
|
|
from sklearn.neighbors import KNeighborsClassifier
|
||
|
|
from sklearn.preprocessing import LabelEncoder
|
||
|
|
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
|
||
|
|
import pickle
|
||
|
|
import statsmodels.api as sm
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import seaborn as sns
|
||
|
|
import os
|
||
|
|
|
||
|
|
curdir = os.getcwd()
|
||
|
|
path_delim = '/'
|
||
|
|
data = pd.read_excel(curdir + path_delim + 'data/tests_24_10_2023_20_53_cleaned.xlsx', sheet_name="data")
|
||
|
|
data = data.dropna()
|
||
|
|
print(data)
|
||
|
|
|
||
|
|
data.plot()
|
||
|
|
|
||
|
|
label_encoder = LabelEncoder()
|
||
|
|
categorical_cols = ['calculatedRatio', 'led1Buffer', 'led1Sample', 'led2Buffer', 'led2Sample']
|
||
|
|
# for col in categorical_cols:
|
||
|
|
# data[col] = label_encoder.fit_transform(data[col])
|
||
|
|
|
||
|
|
X = data[['calculatedRatio', 'led1Buffer', 'led1Sample', 'led2Buffer', 'led2Sample']]
|
||
|
|
y = data['classificationResult']
|
||
|
|
|
||
|
|
print(X)
|
||
|
|
corr = X.corr()
|
||
|
|
print(corr)
|
||
|
|
sm.graphics.plot_corr(corr, xnames=list(corr.columns))
|
||
|
|
plt.show()
|
||
|
|
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
|
||
|
|
|
||
|
|
# Change the classifier to KNeighborsClassifier
|
||
|
|
model = KNeighborsClassifier(n_neighbors=5) # You can adjust the number of neighbors as needed
|
||
|
|
model.fit(X_train, y_train)
|
||
|
|
|
||
|
|
y_pred = model.predict(X_test)
|
||
|
|
|
||
|
|
accuracy = accuracy_score(y_test, y_pred)
|
||
|
|
classification_report_result = classification_report(y_test, y_pred)
|
||
|
|
|
||
|
|
# Calculate the confusion matrix
|
||
|
|
confusion = confusion_matrix(y_test, y_pred)
|
||
|
|
|
||
|
|
# Plot the confusion matrix using Seaborn
|
||
|
|
plt.figure(figsize=(8, 6))
|
||
|
|
sns.heatmap(confusion, annot=True, fmt='d', cmap='Blues', linewidths=0.5)
|
||
|
|
plt.xlabel('Predicted')
|
||
|
|
plt.ylabel('Actual')
|
||
|
|
plt.title('Confusion Matrix')
|
||
|
|
# plt.show()
|
||
|
|
|
||
|
|
print(f"Accuracy: {accuracy}")
|
||
|
|
print("Classification Report:")
|
||
|
|
print(classification_report_result)
|
||
|
|
|
||
|
|
# Accuracy: 0.7502027575020276
|
||
|
|
# Classification Report:
|
||
|
|
# precision recall f1-score support
|
||
|
|
|
||
|
|
# Inconclusive. Repeat with test with lower volume of blood 0.50 0.17 0.25 6
|
||
|
|
# Inconclusive. Very low Absorbance - Repeat test with Higher Blood Volume 0.50 0.48 0.49 21
|
||
|
|
# Negative Borderline. Repeat Test 0.51 0.51 0.51 167
|
||
|
|
# Normal 0.85 0.92 0.88 749
|
||
|
|
# Positive for Sickle Cell. HPLC for Confirmation 0.21 0.17 0.19 41
|
||
|
|
# Sickle Cell Disease 0.67 0.32 0.43 38
|
||
|
|
# Sickle Cell Trait 0.66 0.55 0.60 211
|
||
|
|
|
||
|
|
# accuracy 0.75 1233
|
||
|
|
# macro avg 0.56 0.45 0.48 1233
|
||
|
|
# weighted avg 0.74 0.75 0.74 1233
|
||
|
|
|
||
|
|
# Save the KNeighborsClassifier model
|
||
|
|
with open('kneighbors_classifier_model.pkl', 'wb') as model_file:
|
||
|
|
pickle.dump(model, model_file)
|
||
|
|
|
||
|
|
with open('kneighbors_classifier_model.pkl', 'rb') as model_file:
|
||
|
|
loaded_model = pickle.load(model_file)
|
||
|
|
|
||
|
|
# Define new_data as needed for prediction
|
||
|
|
# new_data = pd.DataFrame({'calculatedRatio': [0.17500836], 'led1Buffer': [24843.33], 'led1Sample': [19678], 'led2Buffer': [26715.33], 'led2Sample': [13842.67]})
|
||
|
|
# new_data = pd.DataFrame({'calculatedRatio': [0.231057205], 'led1Buffer': [23776.33], 'led1Sample': [16286], 'led2Buffer': [26401.67], 'led2Sample': [6952.67]})
|
||
|
|
new_data = pd.DataFrame({'calculatedRatio': [0.175881142], 'led1Buffer': [24256], 'led1Sample': [16303], 'led2Buffer': [27016.33], 'led2Sample': [4612.67]})
|
||
|
|
# new_data = new_data.apply(lambda col: label_encoder.transform(col))
|
||
|
|
print(new_data)
|
||
|
|
predicted_result = loaded_model.predict(new_data)
|
||
|
|
print(predicted_result)
|