机器学习实战:基于3大分类模型的中风病人预测
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公众号:尤而小屋
作者:Peter
编辑:Peter
大家好,我是Peter~
今天给大家分享的是kaggle上一个关于中风疾病案例的数据集建模,文章的主要内容参考导图:
原数据地址:http://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset?datasetId=1120859&sortBy=voteCount&select=healthcare-dataset-stroke-data.csv
导入库
数据基本信息
先把数据导进来,查看数据的基本信息
下面我们查看数据基本信息
In [3]:
df.shape
Out[3]:
(5110, 12)
In [4]:
df.dtypes
Out[4]:
python
id int64
gender object
age float64
hypertension int64
heart_disease int64
ever_married object # 字符型
work_type object
Residence_type object
avg_glucose_level float64
bmi float64
smoking_status object
stroke int64
dtype: object
In [5]:
df.describe() # 描述统计信息
Out[5]:
字段分布
gender统计
In [6]:
``` plt.figure(1, figsize=(12,5))
sns.countplot(y="gender", data=df) plt.show() ```
age分布
In [7]:
px.violin(y=df["age"])
```python fig = px.histogram(df, x="age", color_discrete_sequence=['firebrick'])
fig.show() ```
ever_married
In [9]:
``` plt.figure(1, figsize=(12,5))
sns.countplot(y="ever_married", data=df)
plt.show() ```
本数据集中的结婚人士大约是未结婚的两倍。
work-type
查看不同工作状态的人员数量
In [10]:
```python plt.figure(1, figsize=(12,8))
sns.countplot(y="work_type", data=df)
plt.show() ```
Residence_type
In [11]:
``` plt.figure(1, figsize=(12,8))
sns.countplot(y="Residence_type", data=df)
plt.show() ```
avg_glucose_level
血糖水平的分布
``` fig = px.histogram(df, x="avg_glucose_level", color_discrete_sequence=['firebrick'])
fig.show() ```
可以看到大部分人的血糖还是在100以下,说明是正常的
bmi
bmi指标的分布情况
bmi指标的均值大约在28左右,呈现一定的正态分布
smoking_status
抽烟情况的统计
``` plt.figure(1, figsize=(12,8))
sns.countplot(y="smoking_status", data=df)
plt.show() ```
可以看到抽烟或者曾经抽烟的人相对来说是少一些的
缺失值情况
缺失值统计
df.isnull().sum()
id 0
gender 0
age 0
hypertension 0
heart_disease 0
ever_married 0
work_type 0
Residence_type 0
avg_glucose_level 0
bmi 201
smoking_status 0
stroke 0
dtype: int64
201 / len(df) # 缺失比例
0.03933463796477495
缺失值可视化
缺失值处理
使用决策树回归来预测缺失值的BMI值:通过年龄、性别和现有的bmi值来进行预测填充
python
dt_bmi = Pipeline(steps=[("scale",StandardScaler()), # 数据标准化
("lr",DecisionTreeRegressor(random_state=42))
])
取出3个指标来进行预测填充:
```python X = df[["age","gender","bmi"]].copy()
dic = {"Male":0, "Female":1, "Other":-1}
X["gender"] = X["gender"].map(dic).astype(np.uint8) X.head() ```
取出非缺失值的部分进行训练:
```python
缺失值部分
missing = X[X.bmi.isna()]
非缺失值部分
X = X[~X.bmi.isna()] y = X.pop("bmi") ```
```
模型训练
dt_bmi.fit(X,y) ```
Pipeline(steps=[('scale', StandardScaler()),
('lr', DecisionTreeRegressor(random_state=42))])
In [23]:
```
模型预测
y_pred = dt_bmi.predict(missing[["age","gender"]]) y_pred[:5] ```
Out[23]:
array([29.87948718, 30.55609756, 27.24722222, 30.84186047, 33.14666667])
将预测的值转成Series,并且注意索引号:
predict_bmi = pd.Series(y_pred, index=missing.index)
predict_bmi
python
1 29.879487
8 30.556098
13 27.247222
19 30.841860
27 33.146667
...
5039 32.716000
5048 28.313636
5093 31.459322
5099 28.313636
5105 28.476923
Length: 201, dtype: float64
填充到原来的df数据中:
df.loc[missing.index, "bmi"] = predict_bmi
进行上面的预测和填充之后,我们再次查看缺失值情况,发现已经没有任何缺失值:
df.isnull().sum()
id 0
gender 0
age 0
hypertension 0
heart_disease 0
ever_married 0
work_type 0
Residence_type 0
avg_glucose_level 0
bmi 0
smoking_status 0
stroke 0
dtype: int64
数据EDA
``` variables = [variable for variable in df.columns if variable not in ['id','stroke']]
除去id号和是否中风外的全部字段
variables ```
python
['gender',
'age',
'hypertension',
'heart_disease',
'ever_married',
'work_type',
'Residence_type',
'avg_glucose_level',
'bmi',
'smoking_status']
连续型变量
几点结论:
- 年龄age:整体分布比较均衡,不同年龄段的人数差异小
- 血糖水平:主要集中在100以下
- bmi指标:呈现一定的正态分布
中风和未中风
上面我们查看了连续型变量的分布情况;可以看到bmi呈现明显的左偏态的分布。下面我们对比中风和未中风的情况:
从3个密度图中能够观察到:从上面的密度图中可以看出来:对于是否中风,年龄age是一个最主要的因素
对比不同年龄段的血糖和BMI指数
px.scatter(df,x="age",
y="avg_glucose_level",
color="stroke",
trendline='ols'
)
年龄和血糖、bmi关系
python
px.scatter(df,x="age",
y="bmi",
color="stroke",
trendline='ols'
)
年龄和患病几率
从散点分布图中看到:年龄可能真的是一个比较重要的因素,和BMI以及平均的血糖水平有着一定的关系。
可能随着年龄的增长,风险在增加。果真如此吗?
上面的图形说明了两点:
- 年龄越大,中风的几率的确越来越高
- 中风的几率是非常低的(y轴的值很低),这是由于中风和未中风的样本不均衡造成的
原数据5000个样本中只有249个中风样本,比例接近1:20
样本不均衡
整体属性分布
首先我们剔除gender中为Other的情况
In [34]:
str_only = df[df['stroke'] == 1] # 中风
no_str_only = df[df['stroke'] == 0] # 未中风
In [35]:
len(str_only)
Out[35]:
249
In [36]:
```
剔除other
no_str_only = no_str_only[(no_str_only['gender'] != 'Other')] ```
比较在不同的属性下中风和未中风的情况:
建模
模型baseline
In [38]:
len(str_only)
Out[38]:
249
In [39]:
249 / len(df)
Out[39]:
0.0487279843444227
说明总共有249个人是中风的。本数据的总人数是len(df),根据下面的表达式能够得到本次模型的baseline。
也就说,对于阳性中风患者的召回率,一个好的目标是4.8%。
字段编码
对4个字符型的字段进行编码工作:
In [40]:
```python df['gender'] = df['gender'].replace({'Male':0, 'Female':1, 'Other':-1} ).astype(np.uint8)
df['Residence_type'] = df['Residence_type'].map({'Rural':0, 'Urban':1} ).astype(np.uint8)
df['work_type'] = df['work_type'].map({'Private':0, 'Self-employed':1, 'Govt_job':2, 'children':-1, 'Never_worked':-2} ).astype(np.uint8)
df['ever_married'] = df['ever_married'].map({'No':0,'Yes':1}).astype(np.uint8)
df.head() ```
抽烟状态的独热码转换:
In [41]:
df["smoking_status"].value_counts()
Out[41]:
never smoked 1892
Unknown 1544
formerly smoked 885
smokes 789
Name: smoking_status, dtype: int64
In [42]:
python
df = df.join(pd.get_dummies(df["smoking_status"]))
df.drop("smoking_status",axis=1,inplace=True)
数据分割
In [43]:
```python
选取特征
X = df.drop("stroke",axis=1)
目标变量
y = df['stroke'] from sklearn.model_selection import train_test_split
3-7比例
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.3, random_state=42) ```
上采样
前文中提到,本案例中风和未中风的数据比例接近1:20,在这里我们采样基于SMOTE的上采样方法
In [44]:
oversample = SMOTE()
X_train_smote, y_train_smote = oversample.fit_resample(X_train, y_train.ravel())
In [45]:
len(y_train_smote)
Out[45]:
2914
In [46]:
len(X_train_smote)
Out[46]:
2914
建模
采用3种不同的分类模型来建立模型:Random Forest, SVM, Logisitc Regression
In [47]:
python
rf_pipeline = Pipeline(steps = [('scale',StandardScaler()), # 标准化
('RF',RandomForestClassifier(random_state=42))] # 模型
)
svm_pipeline = Pipeline(steps = [('scale',StandardScaler()),
('SVM',SVC(random_state=42))])
logreg_pipeline = Pipeline(steps = [('scale',StandardScaler()),
('LR',LogisticRegression(random_state=42))])
10折交叉验证
3种模型得分对比
In [49]:
print('随机森林:', rf_cv.mean())
print('支持向量机:',svm_cv.mean())
print('逻辑回归:', logreg_cv.mean())
随机森林: 0.9628909366701726
支持向量机: 0.9363667907023254
逻辑回归: 0.8859930523017683
很明显:随机森林表现的最好!
模型训练fit
In [50]:
``` rf_pipeline.fit(X_train_smote,y_train_smote)
svm_pipeline.fit(X_train_smote,y_train_smote)
logreg_pipeline.fit(X_train_smote,y_train_smote) ```
Out[50]:
Pipeline(steps=[('scale', StandardScaler()),
('LR', LogisticRegression(random_state=42))])
In [51]:
```
3种模型预测
rf_pred =rf_pipeline.predict(X_test) svm_pred = svm_pipeline.predict(X_test) logreg_pred = logreg_pipeline.predict(X_test) ```
评价指标
In [52]:
```
1、混淆矩阵
rf_cm = confusion_matrix(y_test, rf_pred ) svm_cm = confusion_matrix(y_test, svm_pred) logreg_cm = confusion_matrix(y_test, logreg_pred) ```
In [53]:
``` print(rf_cm) print("----") print(svm_cm) print("----") print(logreg_cm) [[3338 66] [ 164 9]]
[[3196 208] [ 148 25]]
[[3138 266] [ 116 57]] ```
print('RF mean :',rf_f1)
print('SVM mean :',svm_f1)
print('LR mean :',logreg_f1)
RF mean : 0.07258064516129033
SVM mean : 0.1231527093596059
LR mean : 0.22983870967741934
随机森林模型的分类报告:
```python from sklearn.metrics import plot_confusion_matrix, classification_report
print(classification_report(y_test,rf_pred))
print('Accuracy Score: ',accuracy_score(y_test,rf_pred)) precision recall f1-score support
0 0.95 0.98 0.97 3404
1 0.12 0.05 0.07 173
accuracy 0.94 3577
macro avg 0.54 0.52 0.52 3577 weighted avg 0.91 0.94 0.92 3577
Accuracy Score: 0.9357003075202683 ```
随机森林模型调参
基于网格搜索的参数调优:
```python from sklearn.model_selection import GridSearchCV
n_estimators =[64,100,128,200] max_features = [2,3,5,7] bootstrap = [True,False]
param_grid = {'n_estimators':n_estimators, 'max_features':max_features, 'bootstrap':bootstrap}
rfc = RandomForestClassifier() ```
``` grid = GridSearchCV(rfc,param_grid)
grid.fit(X_train,y_train) ```
GridSearchCV(estimator=RandomForestClassifier(),
param_grid={'bootstrap': [True, False],
'max_features': [2, 3, 5, 7],
'n_estimators': [64, 100, 128, 200]})
grid.best_params_ # 找到最优的参数
{'bootstrap': False, 'max_features': 3, 'n_estimators': 200}
```python
再次建立随机森林模型
rfc = RandomForestClassifier( max_features=3, n_estimators=200, bootstrap=False)
rfc.fit(X_train_smote,y_train_smote)
rfc_tuned_pred = rfc.predict(X_test) ```
```python
新的分类报告得分
print(classification_report(y_test,rfc_tuned_pred))
print('Accuracy Score: ',accuracy_score(y_test,rfc_tuned_pred)) print('F1 Score: ',f1_score(y_test,rfc_tuned_pred)) precision recall f1-score support
0 0.95 0.98 0.97 3404
1 0.05 0.02 0.03 173
accuracy 0.94 3577
macro avg 0.50 0.50 0.50 3577 weighted avg 0.91 0.94 0.92 3577
Accuracy Score: 0.9362594352809617 F1 Score: 0.025641025641025644 ```
逻辑回归模型调参
```python penalty = ['l1','l2'] C = [0.001, 0.01, 0.1, 1, 10, 100]
log_param_grid = {'penalty': penalty, 'C': C}
logreg = LogisticRegression() grid = GridSearchCV(logreg,log_param_grid) ```
grid.fit(X_train_smote,y_train_smote)
python
GridSearchCV(estimator=LogisticRegression(),
param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100],
'penalty': ['l1', 'l2']})
grid.best_params_
{'C': 1, 'penalty': 'l2'}
```python logreg_pipeline = Pipeline(steps = [('scale',StandardScaler()), ('LR',LogisticRegression(C=1,penalty='l2',random_state=42))])
logreg_pipeline.fit(X_train_smote,y_train_smote) ```
Out[65]:
Pipeline(steps=[('scale', StandardScaler()),
('LR', LogisticRegression(C=1, random_state=42))])
In [66]:
logreg_new_pred = logreg_pipeline.predict(X_test) # 新预测
In [67]:
```python print(classification_report(y_test,logreg_new_pred))
print('Accuracy Score: ',accuracy_score(y_test,logreg_new_pred)) print('F1 Score: ',f1_score(y_test,logreg_new_pred)) precision recall f1-score support
0 0.96 0.92 0.94 3404
1 0.18 0.33 0.23 173
accuracy 0.89 3577
macro avg 0.57 0.63 0.59 3577 weighted avg 0.93 0.89 0.91 3577
Accuracy Score: 0.8932065977075762 F1 Score: 0.22983870967741934 ```
支持向量机调参
In [68]:
```python
svm_param_grid = {
'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf']}
svm = SVC(random_state=42)
grid = GridSearchCV(svm, svm_param_grid) ```
In [69]:
grid.fit(X_train_smote,y_train_smote)
Out[69]:
python
GridSearchCV(estimator=SVC(random_state=42),
param_grid={'C': [0.1, 1, 10, 100, 1000],
'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
'kernel': ['rbf']})
In [70]:
grid.best_params_
Out[70]:
{'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}
In [71]:
```python svm_pipeline = Pipeline(steps = [('scale',StandardScaler()),('SVM',SVC(C=100,gamma=0.0001,kernel='rbf',random_state=42))])
svm_pipeline.fit(X_train_smote,y_train_smote)
svm_tuned_pred = svm_pipeline.predict(X_test) ```
In [72]:
```python print(classification_report(y_test,svm_tuned_pred))
print('Accuracy Score: ',accuracy_score(y_test,svm_tuned_pred)) print('F1 Score: ',f1_score(y_test,svm_tuned_pred)) precision recall f1-score support
0 0.96 0.93 0.94 3404
1 0.16 0.27 0.20 173
accuracy 0.90 3577
macro avg 0.56 0.60 0.57 3577 weighted avg 0.92 0.90 0.91 3577
Accuracy Score: 0.8951635448700028 F1 Score: 0.19700214132762314 ```
结论
- 在交叉验证的过程中,随机森林表现的最好。
- 3种模型的对比:随机森林的精度最好,但是F1-score缺失最低的
- 模型可能存在的特点:更能预测哪些人将会中风,而不是哪些人不会中风
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