（python源码）基于Python搭建RNN-LSTM深度学习模型用于医学疾病诊断.zip

import numpy as np import pandas as pd from matplotlib import pyplot as plt import seaborn as sns from sklearn import metrics import keras.backend as K from sklearn.metrics import roc_curve, auc, precision_score, recall_score, f1_score data_train = pd.read_csv("data.csv") features = ['age'] fig = plt.subplots(figsize=(15, 15)) for i, j in enumerate(features): plt.subplots_adjust(hspace=1.0) sns.countplot(x=j, data=data_train) plt.title("No. of age") plt.show() features = ['gender'] fig = plt.subplots(figsize=(15, 15)) for i, j in enumerate(features): plt.subplots_adjust(hspace=1.0) sns.countplot(x=j, data=data_train) plt.title("No. of gender") plt.show() features = ['heart_failure'] fig = plt.subplots(figsize=(15, 15)) for i, j in enumerate(features): plt.subplots_adjust(hspace=1.0) sns.countplot(x=j, data=data_train) plt.title("No. of heart_failure") plt.show() df_tmp1 = data_train[ ['age', 'gender', 'body_mass_index', 'heart_failure', 'hypertension', 'chronic_obstructic_pulmonary_disease', 'chronic_liver_disease', 'diabetes_mellitus', 'chroinc_kidney_disease', 'charlson', 'emergency', 'surgery', 'acute_kidney_disease']] plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False sns.heatmap(df_tmp1.corr(), cmap="YlGnBu", annot=True) plt.show() df_tmp2 = data_train[ ['APSIII', 'SAPSII', 'non_renal_sofa-1', 'non_renal_sofa-3', 'non_renal_sofa', 'aki_stage', 'creatinine_baseline', 'creatinine-1', 'creatinine-3', 'creatinine', 'urine_output-1', 'urine_output-3', 'urine_output', 'diuretic', 'mechanical_ventalition', 'renal_toxic_drug', 'acute_kidney_disease']] plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False sns.heatmap(df_tmp2.corr(), cmap="YlGnBu", annot=True) plt.show() data_test = pd.read_csv("test.csv") X_train = data_train.drop(['acute_kidney_disease'], axis=1) y_train = data_train['acute_kidney_disease'] import keras.layers as layers X_train = layers.Lambda(lambda X_train: K.expand_dims(X_train, axis=-1))(X_train) X_test = data_test.drop(['acute_kidney_disease'], axis=1) y_test = data_test['acute_kidney_disease'] X_test = layers.Lambda(lambda X_test: K.expand_dims(X_test, axis=-1))(X_test) from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM lstm = Sequential() lstm.add(LSTM(units=32, return_sequences=True, input_shape=(X_train.shape[1], 1))) lstm.add(LSTM(50)) lstm.add(Dense(10, activation='relu')) lstm.add(Dense(1, activation='sigmoid')) lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc']) history = lstm.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=200, batch_size=64) score = lstm.evaluate(X_test, y_test, batch_size=128) from keras.utils import plot_model plot_model(lstm, to_file='model.png') def show_history(history): loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(1, len(loss) + 1) plt.figure(figsize=(12, 4)) plt.subplot(1, 2, 1) plt.plot(epochs, loss, 'r', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Test loss') plt.title('Training and Test loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend() acc = history.history['acc'] val_acc = history.history['val_acc'] plt.subplot(1, 2, 2) plt.plot(epochs, acc, 'r', label='Training acc') plt.plot(epochs, val_acc, 'b', label='Test acc') plt.title('Training and Test accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend() plt.show() y_pred = lstm.predict(X_test, batch_size=10) y_pred = np.round(y_pred) data_test['y_pred']=y_pred data_test.to_excel('data_test_pred.xlsx') show_history(history) def roc_f(y_data, y_score, title): fpr, tpr, threshold = roc_curve(y_data, y_score) roc_auc = auc(fpr, tpr) plt.figure() lw = 2 plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title(title + ' RNN-LSTM Model ') plt.legend(loc="lower right") plt.show() y_train_score = lstm.predict_proba(X_train) y_test_score = lstm.predict_proba(X_test) roc_f(y_train, y_train_score, 'Training') roc_f(y_test, y_test_score, 'Test')