File size: 4,907 Bytes
5e10f6c 4b2a917 5e10f6c 4b2a917 5e10f6c 4b2a917 5e10f6c 4b2a917 5e10f6c 449b6cf 4b2a917 5e10f6c 4b2a917 5e10f6c 4b2a917 5e10f6c 4b2a917 5e10f6c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
import gradio as gr
import numpy as np
import xgboost as xgb
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import (
accuracy_score,
precision_score,
recall_score,
f1_score,
confusion_matrix,
)
from ucimlrepo import fetch_ucirepo
import os
# Paths for saving/loading the model
MODEL_PATH = "heart_disease_model.json"
# Load and preprocess the data
heart_disease = fetch_ucirepo(id=45)
X = heart_disease.data.features
y = np.ravel(heart_disease.data.targets)
# Preprocessing pipeline
imputer = SimpleImputer(strategy="mean")
X = imputer.fit_transform(X)
scaler = StandardScaler()
X = scaler.fit_transform(X)
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
X_resampled, y_resampled, test_size=0.2, random_state=42, stratify=y_resampled
)
# Train or load the model
if os.path.exists(MODEL_PATH):
# Load pre-trained model
model = xgb.Booster()
model.load_model(MODEL_PATH)
else:
# Hyperparameter tuning
param_grid = {
"objective": ["binary:logistic"], # For binary classification
"max_depth": [4, 5, 6],
"learning_rate": [0.01, 0.05, 0.1],
"n_estimators": [100, 200, 300],
"subsample": [0.8, 1.0],
"colsample_bytree": [0.8, 1.0],
"gamma": [0, 1, 5],
"lambda": [1, 2, 3],
"alpha": [0, 1],
}
model = xgb.XGBClassifier(use_label_encoder=False, eval_metric="mlogloss")
grid_search = GridSearchCV(
estimator=model, param_grid=param_grid, scoring="accuracy", cv=5, verbose=1
)
grid_search.fit(X_train, y_train)
# Best model
best_model = grid_search.best_estimator_
best_model.save_model(MODEL_PATH)
# Load the best model
model = xgb.Booster()
model.load_model(MODEL_PATH)
# Evaluate model
X_test_dmatrix = xgb.DMatrix(X_test)
y_pred = model.predict(X_test_dmatrix)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average="weighted")
recall = recall_score(y_test, y_pred, average="weighted")
f1 = f1_score(y_test, y_pred, average="weighted")
conf_matrix = confusion_matrix(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")
print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"F1 Score: {f1:.2f}")
print("Confusion Matrix:")
print(conf_matrix)
# Define prediction function
def predict(
age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal
):
# Convert string values to numeric where needed
sex = int(sex)
cp = int(cp)
fbs = int(fbs)
restecg = int(restecg)
exang = int(exang)
slope = int(slope)
thal = int(thal)
# Combine inputs into a single feature list
features = np.array(
[
age,
sex,
cp,
trestbps,
chol,
fbs,
restecg,
thalach,
exang,
oldpeak,
slope,
ca,
thal,
]
).reshape(1, -1)
# Preprocess the inputs
features = scaler.transform(imputer.transform(features))
# Predict using the trained model
dmatrix = xgb.DMatrix(features)
prediction = model.predict(dmatrix)
return int(prediction[0])
# Gradio interface
feature_inputs = [
gr.Number(label="Age (years)"),
gr.Radio(label="Sex", choices=["0", "1"], type="value"), # Male: 1, Female: 0
gr.Radio(label="Chest Pain Type (cp)", choices=["0", "1", "2", "3"], type="value"),
gr.Number(label="Resting Blood Pressure (mm Hg)"),
gr.Number(label="Serum Cholestoral (mg/dl)"),
gr.Radio(
label="Fasting Blood Sugar > 120 mg/dl (fbs)", choices=["0", "1"], type="value"
),
gr.Radio(
label="Resting ECG Results (restecg)", choices=["0", "1", "2"], type="value"
),
gr.Number(label="Maximum Heart Rate Achieved (thalach)"),
gr.Radio(label="Exercise Induced Angina (exang)", choices=["0", "1"], type="value"),
gr.Number(label="ST Depression Induced by Exercise (oldpeak)"),
gr.Radio(
label="Slope of the Peak Exercise ST Segment (slope)",
choices=["0", "1", "2"],
type="value",
),
gr.Number(label="Number of Major Vessels Colored by Fluoroscopy (ca)"),
gr.Radio(label="Thalassemia (thal)", choices=["0", "1", "2", "3"], type="value"),
]
interface = gr.Interface(
fn=predict,
inputs=feature_inputs,
outputs="label",
title="Heart Disease Prediction",
description=(
"Predicts heart disease based on patient information. "
"Provide the required features to get a diagnosis prediction."
),
)
if __name__ == "__main__":
interface.launch()
|