基于机器学习的临床决策支持-ANN
【摘要】
声明：本文示例来自于GitHub用户vkasojhaa的项目，一切权利归其所有，此处仅是自己学习分享。
实现了基于机器学习的乳腺癌的恶性和良性预测，比较了不同机器学习算法之间的性能。主要目的是评估在每种算法的准确性和效率方面对数据进行分类的正确性。
loss
# 损失值：预估值与实际值之间的均方差
optimizer
# 优化器
trainer = ...
声明：本文示例来自于GitHub用户vkasojhaa的项目，一切权利归其所有，此处仅是自己学习分享。
实现了基于机器学习的乳腺癌的恶性和良性预测，比较了不同机器学习算法之间的性能。主要目的是评估在每种算法的准确性和效率方面对数据进行分类的正确性。
loss
# 损失值：预估值与实际值之间的均方差
optimizer
# 优化器
trainer = optimizer.minimize(loss)
# 训练：最小化损失函数
基于机器学习(ANN)的乳腺癌预测
代码示例
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#导入依赖库
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#!/usr/bin/python3
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from __future__ import print_function
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import keras
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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from keras import backend as K
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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from IPython.display import clear_output
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from keras.utils import plot_model
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#载入数据并进行数据预处理
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data = pd.read_csv("data.csv")
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data.head()
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data.drop('id',axis=1,inplace=True)
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data.drop('Unnamed: 32',axis=1,inplace=True)
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data['diagnosis'] = data['diagnosis'].map({'M':1,'B':0})
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data.head()
print("Row, Col", data.shape)# (row,col)
(data['diagnosis'][:398]==1).sum(),(data['diagnosis'][:398]==0).sum()
(data['diagnosis'][398:]==1).sum(),(data['diagnosis'][398:]==0).sum()
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mat=data.as_matrix()
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mat.shape
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mat
模型训练：
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#Using 2 Sigmoid Layers and RMSprop optimizer
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model2 = Sequential()
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model2.add(Dense(500, activation='sigmoid', use_bias=True, input_shape=(30,)))
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model2.add(Dense(1, activation='sigmoid'))
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keras.optimizers.RMSprop(lr=0.01, rho=0.9, epsilon=None, decay=0.0)
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model2.compile(optimizer='rmsprop',loss='binary_crossentropy', metrics=['accuracy'])
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history01 = model2.fit(mat[:,1:],mat[:,0], validation_split=0.3,shuffle=False,epochs=3000, batch_size=128,verbose=0)
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score = model2.evaluate(mat[:398,1:],mat[:398,0], verbose=0, batch_size=128)
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print('Train loss:', score[0])
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print('Train accuracy:', score[1])
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Train loss: 0.00821199454791287
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Train accuracy: 1.0
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score = model2.evaluate(mat[398:,1:],mat[398:,0], verbose=0, batch_size=128)
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print('Validation loss:', score[0])
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print('Validation accuracy:', score[1])
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Validation loss: 0.1262894694568121
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Validation accuracy: 0.9473684154755888
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plt.plot(history01.history['acc'], label='acc')
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plt.plot(history01.history['val_acc'], label='val_acc')
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plt.legend()
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plt.show()
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plt.plot(history01.history['loss'], label='loss')
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plt.plot(history01.history['val_loss'], label='val_loss')
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plt.legend()
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plt.show()
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count=0
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for i in history01.history['acc']:
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if i>0.99:
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count+=1
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print(count)
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#Using 3 Sigmoid Layers and RMSprop optimizer
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model3 = Sequential()
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model3.add(Dense(500, activation='sigmoid', use_bias=True, input_shape=(30,)))
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model3.add(Dense(500, activation='sigmoid', use_bias=True))
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model3.add(Dense(1, activation='sigmoid'))
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keras.optimizers.RMSprop(lr=0.01, rho=0.9, epsilon=None, decay=0.0)
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model3.compile(optimizer='rmsprop',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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history0 = model3.fit(mat[:,1:],mat[:,0], validation_split=0.3,shuffle=False,epochs=3000, batch_size=128, verbose=0)
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score2 = model3.evaluate(mat[:398,1:],mat[:398,0], verbose=0, batch_size=128)
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print('Train loss:', score2[0])
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print('Train accuracy:', score2[1])
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Train loss: 0.011901359889894986
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Train accuracy: 0.992462311557789
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score2 = model3.evaluate(mat[398:,1:],mat[398:,0], verbose=0, batch_size=128)
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print('Validation loss:', score2[0])
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print('Validation accuracy:', score2[1])
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Validation loss: 0.0804629116945448
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Validation accuracy: 0.9707602311296073
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plt.plot(history0.history['acc'], label='acc')
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plt.plot(history0.history['val_acc'], label='val_acc')
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plt.legend()
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plt.show()
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plt.plot(history0.history['loss'], label='loss')
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plt.plot(history0.history['val_loss'], label='val_loss')
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plt.legend()
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plt.show()
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count=0
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for i in history0.history['acc']:
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if i>0.99:
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count+=1
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print(count)
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#Using 3 Sigmoid Layers and RMSprop optimizer
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model3 = Sequential()
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model3.add(Dense(500, activation='sigmoid', use_bias=True, input_shape=(30,)))
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model3.add(Dense(500, activation='sigmoid', use_bias=True))
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model3.add(Dense(1, activation='sigmoid'))
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keras.optimizers.RMSprop(lr=0.01, rho=0.9, epsilon=None, decay=0.0)
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model3.compile(optimizer='rmsprop',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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history0 = model3.fit(mat[:,1:],mat[:,0], validation_split=0.3,shuffle=False,epochs=3000, batch_size=128, verbose=0)
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score2 = model3.evaluate(mat[:398,1:],mat[:398,0], verbose=0, batch_size=128)
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print('Train loss:', score2[0])
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print('Train accuracy:', score2[1])
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Validation loss: 0.0804629116945448
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Validation accuracy: 0.9707602311296073
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plt.plot(history0.history['acc'], label='acc')
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plt.plot(history0.history['val_acc'], label='val_acc')
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plt.legend()
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plt.show()
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plt.plot(history0.history['loss'], label='loss')
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plt.plot(history0.history['val_loss'], label='val_loss')
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plt.legend()
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plt.show()
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count=0
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for i in history0.history['acc']:
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if i>0.99:
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count+=1
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print(count)
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#Using 4 Sigmoid Layers and RMSprop optimizer
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model4 = Sequential()
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model4.add(Dense(500, activation='sigmoid', use_bias=True, input_shape=(30,)))
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model4.add(Dense(500, activation='sigmoid', use_bias=True))
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model4.add(Dense(500, activation='sigmoid', use_bias=True))
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model4.add(Dense(1, activation='sigmoid'))
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keras.optimizers.RMSprop(lr=0.01, rho=0.9, epsilon=None, decay=0.0)
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model4.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
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history1 = model4.fit(mat[:,1:],mat[:,0], validation_split=0.3,shuffle=False,epochs=3000, batch_size=128, verbose=0)
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#model.evaluate(mat[:,1:],mat[:,0], batch_size=None, verbose=1, sample_weight=None, steps=None)
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score = model4.evaluate(mat[:398,1:],mat[:398,0], verbose=0, batch_size=128)
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print('Train loss:', score[0])
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print('Train accuracy:', score[1])
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Train loss: 0.1454299822000403
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Train accuracy: 0.9346733668341709
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score = model4.evaluate(mat[398:,1:],mat[398:,0], verbose=0, batch_size=128)
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print('Validation loss:', score[0])
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print('Validation accuracy:', score[1])
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Validation loss: 0.33671002412400053
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Validation accuracy: 0.8830409339296887
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plt.plot(history1.history['acc'], label='acc')
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plt.plot(history1.history['val_acc'], label='val_acc')
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plt.legend()
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plt.show()
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plt.plot(history1.history['loss'], label='loss')
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plt.plot(history1.history['val_loss'], label='val_loss')
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plt.legend()
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plt.show()
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count=0
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for i in history1.history['acc']:
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if i>0.99:
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count+=1
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print(count)
Connecting artificial intelligence (AI) with pharmaceutical sciences.
参考资料：
https://github.com/vkasojhaa/Clinical-Decision-Support-using-Machine-Learning
文章来源: drugai.blog.csdn.net，作者：DrugAI，版权归原作者所有，如需转载，请联系作者。
原文链接：drugai.blog.csdn.net/article/details/105683686
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