心律失常的分类 : 数据探索篇

本项目中使用的数据集由分布在 16 个类别的 452 个不同示例组成。在 452 个例子中，245 个是“正常”人。我们还有 12 种不同类型的心律失常。在所有这些类型的心律失常中，最具代表性的是“冠状动脉疾病”和“Rjgbt 束支传导阻滞”。

  • 我们有279个特征，包括患者的年龄、性别、体重、身高以及心电图的相关信息。我们明确观察到，与可用示例的数量相比，特征的数量相对较多。

1. 数据争论¶

导入必要的库¶

In [72]:
import pandas as pd
import numpy as np
import scipy as sp
import math as mt
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.impute import SimpleImputer

数据读取¶

首先读取数据文件并创建一个数据框来放置我们将运行的所有模型的输出。这样做的目的是为了方便我们比较模型。

In [73]:
#读取csv文件

df=pd.read_csv("arrhythmia.csv",header=None)

一.查看数据集的前五行

In [74]:
df.head()
Out[74]:
0 1 2 3 4 5 6 7 8 9 ... 270 271 272 273 274 275 276 277 278 279
0 75 0 190 80 91 193 371 174 121 -16 ... 0.0 9.0 -0.9 0.0 0.0 0.9 2.9 23.3 49.4 8
1 56 1 165 64 81 174 401 149 39 25 ... 0.0 8.5 0.0 0.0 0.0 0.2 2.1 20.4 38.8 6
2 54 0 172 95 138 163 386 185 102 96 ... 0.0 9.5 -2.4 0.0 0.0 0.3 3.4 12.3 49.0 10
3 55 0 175 94 100 202 380 179 143 28 ... 0.0 12.2 -2.2 0.0 0.0 0.4 2.6 34.6 61.6 1
4 75 0 190 80 88 181 360 177 103 -16 ... 0.0 13.1 -3.6 0.0 0.0 -0.1 3.9 25.4 62.8 7

5 rows × 280 columns

二.查看数据集的最后五行

In [75]:
df.tail()
Out[75]:
0 1 2 3 4 5 6 7 8 9 ... 270 271 272 273 274 275 276 277 278 279
447 53 1 160 70 80 199 382 154 117 -37 ... 0.0 4.3 -5.0 0.0 0.0 0.7 0.6 -4.4 -0.5 1
448 37 0 190 85 100 137 361 201 73 86 ... 0.0 15.6 -1.6 0.0 0.0 0.4 2.4 38.0 62.4 10
449 36 0 166 68 108 176 365 194 116 -85 ... 0.0 16.3 -28.6 0.0 0.0 1.5 1.0 -44.2 -33.2 2
450 32 1 155 55 93 106 386 218 63 54 ... -0.4 12.0 -0.7 0.0 0.0 0.5 2.4 25.0 46.6 1
451 78 1 160 70 79 127 364 138 78 28 ... 0.0 10.4 -1.8 0.0 0.0 0.5 1.6 21.3 32.8 1

5 rows × 280 columns

三。数据框的基本描述

In [76]:
#数据集维度。

df.shape
Out[76]:
(452, 280)
In [77]:
#数据框的简明摘要。

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 452 entries, 0 to 451
Columns: 280 entries, 0 to 279
dtypes: float64(120), int64(155), object(5)
memory usage: 988.9+ KB
In [78]:
#数据帧的描述性统计。

df.describe().T
Out[78]:
count mean std min 25% 50% 75% max
0 452.0 46.471239 16.466631 0.0 36.00 47.00 58.000 83.0
1 452.0 0.550885 0.497955 0.0 0.00 1.00 1.000 1.0
2 452.0 166.188053 37.170340 105.0 160.00 164.00 170.000 780.0
3 452.0 68.170354 16.590803 6.0 59.00 68.00 79.000 176.0
4 452.0 88.920354 15.364394 55.0 80.00 86.00 94.000 188.0
... ... ... ... ... ... ... ... ...
275 452.0 0.514823 0.347531 -0.8 0.40 0.50 0.700 2.4
276 452.0 1.222345 1.426052 -6.0 0.50 1.35 2.100 6.0
277 452.0 19.326106 13.503922 -44.2 11.45 18.10 25.825 88.8
278 452.0 29.473230 18.493927 -38.6 17.55 27.90 41.125 115.9
279 452.0 3.880531 4.407097 1.0 1.00 1.00 6.000 16.0

275 rows × 8 columns

处理缺失值¶


在浏览数据集时，我们观察到在 279 个属性中，有 5 个属性在表单中缺失值的格式为 '？'。我们将遵循的方法是，首先使用nan替换 '?'，然后使用 isnull检测空值的数量

检查数据集中的空值

In [79]:
#统计空值总数

pd.isnull(df).sum().sum()
Out[79]:
0
In [80]:
#替换？ 

df = df.replace('?', np.NaN)
In [81]:
#最终统计数据集中空值的总数

nu=pd.isnull(df).sum().sum()
nu
Out[81]:
408

可视化缺失数据的分布：

In [82]:
pd.isnull(df).sum().plot()
plt.xlabel('Columns')
plt.ylabel('Total number of null value in each column')
Out[82]:
Text(0, 0.5, 'Total number of null value in each column')
In [83]:
#放大

pd.isnull(df).sum()[7:17].plot(kind="pie")
plt.xlabel('Columns')
plt.ylabel('Total number of null value in each column')
Out[83]:
Text(0, 0.5, 'Total number of null value in each column')
In [84]:
#可视化缺失值的确切列

pd.isnull(df).sum()[9:16].plot(kind="bar")
plt.xlabel('Columns')
plt.ylabel('Total number of null value in each column')
Out[84]:
Text(0, 0.5, 'Total number of null value in each column')
In [85]:
#删除第 13 列，因为它包含许多缺失值。

df.drop(columns = 13, inplace=True)

使用均值策略和missing_values类型进行插补

In [86]:
# 进行复制以避免更改原始数据（插补时）

new_df = df.copy()
In [87]:
# 创建新列来指示将估算的内容

cols_with_missing = (col for col in new_df.columns 
                                 if new_df[col].isnull().any())
for col in cols_with_missing:
    new_df[col] = new_df[col].isnull()
In [88]:
# 插补

my_imputer = SimpleImputer()
new_df = pd.DataFrame(my_imputer.fit_transform(new_df))
new_df.columns = df.columns
In [89]:
# 估算数据框

new_df.head()
Out[89]:
0 1 2 3 4 5 6 7 8 9 ... 270 271 272 273 274 275 276 277 278 279
0 75.0 0.0 190.0 80.0 91.0 193.0 371.0 174.0 121.0 -16.0 ... 0.0 9.0 -0.9 0.0 0.0 0.9 2.9 23.3 49.4 8.0
1 56.0 1.0 165.0 64.0 81.0 174.0 401.0 149.0 39.0 25.0 ... 0.0 8.5 0.0 0.0 0.0 0.2 2.1 20.4 38.8 6.0
2 54.0 0.0 172.0 95.0 138.0 163.0 386.0 185.0 102.0 96.0 ... 0.0 9.5 -2.4 0.0 0.0 0.3 3.4 12.3 49.0 10.0
3 55.0 0.0 175.0 94.0 100.0 202.0 380.0 179.0 143.0 28.0 ... 0.0 12.2 -2.2 0.0 0.0 0.4 2.6 34.6 61.6 1.0
4 75.0 0.0 190.0 80.0 88.0 181.0 360.0 177.0 103.0 -16.0 ... 0.0 13.1 -3.6 0.0 0.0 -0.1 3.9 25.4 62.8 7.0

5 rows × 279 columns

In [90]:
# 具有零 null 值的数据集。

pd.isnull(new_df).sum().sum()
Out[90]:
0

生成最终数据集

In [91]:
#创建列名

final_df_columns=["Age","Sex","Height","Weight","QRS_Dur",
"P-R_Int","Q-T_Int","T_Int","P_Int","QRS","T","P","J","Heart_Rate",
"Q_Wave","R_Wave","S_Wave","R'_Wave","S'_Wave","Int_Def","Rag_R_Nom",
"Diph_R_Nom","Rag_P_Nom","Diph_P_Nom","Rag_T_Nom","Diph_T_Nom", 
"DII00", "DII01","DII02", "DII03", "DII04","DII05","DII06","DII07","DII08","DII09","DII10","DII11",
"DIII00","DIII01","DIII02", "DIII03", "DIII04","DIII05","DIII06","DIII07","DIII08","DIII09","DIII10","DIII11",
"AVR00","AVR01","AVR02","AVR03","AVR04","AVR05","AVR06","AVR07","AVR08","AVR09","AVR10","AVR11",
"AVL00","AVL01","AVL02","AVL03","AVL04","AVL05","AVL06","AVL07","AVL08","AVL09","AVL10","AVL11",
"AVF00","AVF01","AVF02","AVF03","AVF04","AVF05","AVF06","AVF07","AVF08","AVF09","AVF10","AVF11",
"V100","V101","V102","V103","V104","V105","V106","V107","V108","V109","V110","V111",
"V200","V201","V202","V203","V204","V205","V206","V207","V208","V209","V210","V211",
"V300","V301","V302","V303","V304","V305","V306","V307","V308","V309","V310","V311",
"V400","V401","V402","V403","V404","V405","V406","V407","V408","V409","V410","V411",
"V500","V501","V502","V503","V504","V505","V506","V507","V508","V509","V510","V511",
"V600","V601","V602","V603","V604","V605","V606","V607","V608","V609","V610","V611",
"JJ_Wave","Amp_Q_Wave","Amp_R_Wave","Amp_S_Wave","R_Prime_Wave","S_Prime_Wave","P_Wave","T_Wave",
"QRSA","QRSTA","DII170","DII171","DII172","DII173","DII174","DII175","DII176","DII177","DII178","DII179",
"DIII180","DIII181","DIII182","DIII183","DIII184","DIII185","DIII186","DIII187","DIII188","DIII189",
"AVR190","AVR191","AVR192","AVR193","AVR194","AVR195","AVR196","AVR197","AVR198","AVR199",
"AVL200","AVL201","AVL202","AVL203","AVL204","AVL205","AVL206","AVL207","AVL208","AVL209",
"AVF210","AVF211","AVF212","AVF213","AVF214","AVF215","AVF216","AVF217","AVF218","AVF219",
"V1220","V1221","V1222","V1223","V1224","V1225","V1226","V1227","V1228","V1229",
"V2230","V2231","V2232","V2233","V2234","V2235","V2236","V2237","V2238","V2239",
"V3240","V3241","V3242","V3243","V3244","V3245","V3246","V3247","V3248","V3249",
"V4250","V4251","V4252","V4253","V4254","V4255","V4256","V4257","V4258","V4259",
"V5260","V5261","V5262","V5263","V5264","V5265","V5266","V5267","V5268","V5269",
"V6270","V6271","V6272","V6273","V6274","V6275","V6276","V6277","V6278","V6279","class"]
In [92]:
#将列名称添加到数据集

new_df.columns=final_df_columns
new_df.to_csv("new data with target class.csv")
new_df.head()
Out[92]:
Age Sex Height Weight QRS_Dur P-R_Int Q-T_Int T_Int P_Int QRS ... V6271 V6272 V6273 V6274 V6275 V6276 V6277 V6278 V6279 class
0 75.0 0.0 190.0 80.0 91.0 193.0 371.0 174.0 121.0 -16.0 ... 0.0 9.0 -0.9 0.0 0.0 0.9 2.9 23.3 49.4 8.0
1 56.0 1.0 165.0 64.0 81.0 174.0 401.0 149.0 39.0 25.0 ... 0.0 8.5 0.0 0.0 0.0 0.2 2.1 20.4 38.8 6.0
2 54.0 0.0 172.0 95.0 138.0 163.0 386.0 185.0 102.0 96.0 ... 0.0 9.5 -2.4 0.0 0.0 0.3 3.4 12.3 49.0 10.0
3 55.0 0.0 175.0 94.0 100.0 202.0 380.0 179.0 143.0 28.0 ... 0.0 12.2 -2.2 0.0 0.0 0.4 2.6 34.6 61.6 1.0
4 75.0 0.0 190.0 80.0 88.0 181.0 360.0 177.0 103.0 -16.0 ... 0.0 13.1 -3.6 0.0 0.0 -0.1 3.9 25.4 62.8 7.0

5 rows × 279 columns

In [93]:
# 删除目标属性以创建最终数据帧。

final_df = new_df.drop(columns ="class")
final_df.to_csv("FInal Dataset with dropped class Attribute.csv")
final_df.head()
Out[93]:
Age Sex Height Weight QRS_Dur P-R_Int Q-T_Int T_Int P_Int QRS ... V6270 V6271 V6272 V6273 V6274 V6275 V6276 V6277 V6278 V6279
0 75.0 0.0 190.0 80.0 91.0 193.0 371.0 174.0 121.0 -16.0 ... -0.3 0.0 9.0 -0.9 0.0 0.0 0.9 2.9 23.3 49.4
1 56.0 1.0 165.0 64.0 81.0 174.0 401.0 149.0 39.0 25.0 ... -0.5 0.0 8.5 0.0 0.0 0.0 0.2 2.1 20.4 38.8
2 54.0 0.0 172.0 95.0 138.0 163.0 386.0 185.0 102.0 96.0 ... 0.9 0.0 9.5 -2.4 0.0 0.0 0.3 3.4 12.3 49.0
3 55.0 0.0 175.0 94.0 100.0 202.0 380.0 179.0 143.0 28.0 ... 0.1 0.0 12.2 -2.2 0.0 0.0 0.4 2.6 34.6 61.6
4 75.0 0.0 190.0 80.0 88.0 181.0 360.0 177.0 103.0 -16.0 ... -0.4 0.0 13.1 -3.6 0.0 0.0 -0.1 3.9 25.4 62.8

5 rows × 278 columns

In [94]:
pd.isnull(final_df).sum().sum()
Out[94]:
0

2.探索性数据分析（EDA）¶

列出所有 16 个类名称的列表。

In [95]:
#带有类名的列表

class_names = ["Normal", 
               "Ischemic changes (CAD)", 
               "Old Anterior Myocardial Infraction",
               "Old Inferior Myocardial Infraction",
               "Sinus tachycardy", 
               "Sinus bradycardy", 
               "Ventricular Premature Contraction (PVC)",
               "Supraventricular Premature Contraction",
               "Left Boundle branch block",
               "Right boundle branch block",
               "1.Degree AtrioVentricular block",
               "2.Degree AV block",
               "3.Degree AV block",
               "Left Ventricule hypertrophy",
               "Atrial Fibrillation or Flutter",
               "Others"]

分析数据集并检查每个类别有多少个示例：¶

In [96]:
# 仅检查属性的数据类型

final_df[["Age","Heart_Rate"]].dtypes
Out[96]:
Age           float64
Heart_Rate    float64
dtype: object

我们需要根据类属性对数据集进行排序，以计算每个类可用的实例数量

In [97]:
#根据类属性进行排序。

t=new_df.sort_values(by=["class"])
In [98]:
# 计算每个类的实例数量

la = t["class"].value_counts(sort=False).tolist()
la
Out[98]:
[245, 44, 15, 15, 13, 25, 3, 2, 9, 50, 4, 5, 22]
In [99]:
#关于类的数据集可视化

labels = class_names
values = la[0:10]
values.extend([0,0,0])
values.extend(la[10:13])
print(values)
#我们用以 10 为底的对数对数据进行标准化，以便能够“以合适的方式绘制它们”
Log_Norm = []
for i in values:
    Log_Norm.append(mt.log10(i+1))
fig1, ax1 = plt.subplots(figsize=(16,9))
patches = plt.pie(Log_Norm, autopct='%1.1f%%', startangle=90)

leg = plt.legend( loc = 'best', labels=['%s, %1.1f %%' % (l, s) for l, s in zip(labels, Log_Norm)])
plt.axis('equal')

for text in leg.get_texts():
    plt.setp(text, color = 'Black')
plt.tight_layout()
plt.show()

[245, 44, 15, 15, 13, 25, 3, 2, 9, 50, 0, 0, 0, 4, 5, 22]
In [100]:
fdict = {}
j = 0
for i in labels:
    fdict[i] = Log_Norm[j]
    j+=1
fdf = pd.DataFrame(fdict,index=[0])
fdf = fdf.rename(index={0: ''})
fig, ax = plt.subplots(figsize=(15,10))
fdf.plot(kind="bar",ax=ax)
ax.set_title("(Log)-Histogram of the Classes Distributions ")
ax.set_ylabel('Number of examples')
ax.set_xlabel('Classes')
fig.tight_layout()
plt.show()
In [101]:
#分组描述
import scipy.stats as sp
grouped_mean = new_df.groupby(["class"]).mean()
grouped_median = new_df.groupby(["class"]).median()
grouped_mode =  new_df.groupby(["class"]).agg(lambda x: sp.mode(x, axis=None, keepdims=True)[0])
gouped_std = new_df.groupby(['class']).std()
In [102]:
grouped_median.head()
Out[102]:
Age Sex Height Weight QRS_Dur P-R_Int Q-T_Int T_Int P_Int QRS ... V6270 V6271 V6272 V6273 V6274 V6275 V6276 V6277 V6278 V6279
class
1.0 46.0 1.0 163.0 68.0 84.0 156.0 367.0 160.0 91.0 41.0 ... -0.1 0.0 9.1 -0.9 0.0 0.0 0.5 1.6 19.10 31.7
2.0 55.0 1.0 160.5 70.0 90.5 159.0 366.0 176.5 91.0 37.5 ... -0.8 0.0 9.6 -1.3 0.0 0.0 0.5 -0.8 20.05 11.8
3.0 50.0 0.0 170.0 74.0 93.0 163.0 351.0 196.0 96.0 48.0 ... -0.2 0.0 6.3 -2.0 0.0 0.0 0.8 0.6 12.30 19.3
4.0 57.0 0.0 165.0 76.0 93.0 157.0 357.0 161.0 95.0 -2.0 ... -0.2 -0.7 6.3 -0.5 0.0 0.0 0.7 0.3 10.90 12.0
5.0 34.0 1.0 160.0 51.0 82.0 155.0 302.0 153.0 81.0 52.0 ... -0.3 0.0 5.1 -2.1 0.0 0.0 0.5 0.9 6.00 10.7

5 rows × 278 columns

In [103]:
#相关热图

sns.heatmap(new_df[['Age', 'Sex', 'Height', 'Weight','class']].corr(), cmap='CMRmap',center=0)
plt.show()

处理异常值和数据可视化¶

In [104]:
#寻找成对关系和异常值

g = sns.PairGrid(final_df, vars=['Age', 'Sex', 'Height', 'Weight'],
                 hue='Sex', palette='BrBG')
g.map(plt.scatter, alpha=0.8)
g.add_legend();

根据散点图，“身高”和“体重”属性几乎没有异常值。检查身高和体重的最大值

In [105]:
sorted(final_df['Height'], reverse=True)[:10]
Out[105]:
[780.0, 608.0, 190.0, 190.0, 190.0, 188.0, 186.0, 186.0, 186.0, 185.0]

世界上最高的人是 272 厘米（1940 年）。他的追随者是 267 厘米（1905 年）和 263.5 厘米（1969 年）。将 7​​80 和 608 分别替换为 180 和 108 厘米

In [106]:
final_df['Height']=final_df['Height'].replace(608,108)
final_df['Height']=final_df['Height'].replace(780,180)
In [107]:
sorted(final_df['Weight'], reverse=True)[:10]
Out[107]:
[176.0, 124.0, 110.0, 106.0, 105.0, 105.0, 104.0, 104.0, 100.0, 98.0]

176 公斤 是可能的重量。所以我们将它们保留在数据框中。

In [108]:
g = sns.PairGrid(final_df, vars=['Age', 'Sex', 'Height', 'Weight'],
                 hue='Sex', palette='RdBu_r')
g.map(plt.scatter, alpha=0.8)
g.add_legend();
In [109]:
sns.boxplot(data=final_df[["QRS_Dur","P-R_Int","Q-T_Int","T_Int","P_Int"]]);

PR 间期是指从 P 波开始一直延伸到 QRS 波群开始的时间段（以毫秒为单位）；它的持续时间通常在 120 到 200 毫秒之间。

In [110]:
final_df['P-R_Int'].value_counts().sort_index().head().plot(kind='bar')
plt.xlabel('P-R Interval Values')
plt.ylabel('Count');
In [111]:
final_df['P-R_Int'].value_counts().sort_index().tail().plot(kind='bar')
plt.xlabel('P-R Interval Values')
plt.ylabel('Count');

PR 间隔数据包括异常值 0(x18)。我会保留它们¶

QT 间期是心脏电周期中 Q 波开始和 T 波结束之间时间的测量。 Q-T 间隔框中出现的异常值数据可能与 T 间隔数据的异常值有关。

In [112]:
final_df['T_Int'].value_counts().sort_index(ascending=False).head().plot(kind='bar')
plt.xlabel('T Interval Values')
plt.ylabel('Count');
In [113]:
final_df['T_Int'].value_counts().sort_index(ascending=False).tail().plot(kind='bar')
plt.xlabel('T Interval Values')
plt.ylabel('Count');
In [114]:
sns.boxplot(data=final_df[["QRS","T","P","J","Heart_Rate"]]);
In [115]:
sns.boxplot(data=final_df[["Q_Wave","R_Wave","S_Wave"]])
sns.swarmplot(data=final_df[["Q_Wave","R_Wave","S_Wave"]]);

d:\anaconda\envs\pytorch2.0\Lib\site-packages\seaborn\categorical.py:3544: UserWarning: 78.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
d:\anaconda\envs\pytorch2.0\Lib\site-packages\seaborn\categorical.py:3544: UserWarning: 46.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
d:\anaconda\envs\pytorch2.0\Lib\site-packages\seaborn\categorical.py:3544: UserWarning: 52.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
In [116]:
sns.boxplot(data=final_df[["R'_Wave","S'_Wave","Int_Def","Rag_R_Nom"]]);

S'Wave 有 0，不是 NaN。因此，我们不能假设它包含异常值。¶

In [117]:
final_df["R'_Wave"].value_counts().sort_index(ascending=False)
Out[117]:
24.0      1
16.0      1
12.0      2
0.0     448
Name: R'_Wave, dtype: int64
In [118]:
final_df["S'_Wave"].value_counts().sort_index(ascending=False)
Out[118]:
0.0    452
Name: S'_Wave, dtype: int64
In [119]:
final_df["Rag_R_Nom"].value_counts().sort_index(ascending=False)
Out[119]:
1.0      1
0.0    451
Name: Rag_R_Nom, dtype: int64
In [120]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["Diph_R_Nom","Rag_P_Nom","Diph_P_Nom","Rag_T_Nom","Diph_T_Nom"]]);
In [121]:
final_df["Diph_R_Nom"].value_counts().sort_index(ascending=False)
Out[121]:
1.0      5
0.0    447
Name: Diph_R_Nom, dtype: int64
In [122]:
final_df["Rag_P_Nom"].value_counts().sort_index(ascending=False)
Out[122]:
1.0      5
0.0    447
Name: Rag_P_Nom, dtype: int64
In [123]:
final_df["Diph_P_Nom"].value_counts().sort_index(ascending=False)
Out[123]:
1.0      2
0.0    450
Name: Diph_P_Nom, dtype: int64
In [124]:
final_df["Rag_T_Nom"].value_counts().sort_index(ascending=False)
Out[124]:
1.0      2
0.0    450
Name: Rag_T_Nom, dtype: int64
In [125]:
final_df["Diph_T_Nom"].value_counts().sort_index(ascending=False)
Out[125]:
1.0      4
0.0    448
Name: Diph_T_Nom, dtype: int64
In [126]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["DII00", "DII01","DII02", "DII03", "DII04","DII05","DII06","DII07","DII08","DII09","DII10","DII11"]]);
In [127]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["DIII00","DIII01","DIII02", "DIII03", "DIII04","DIII05","DIII06",
                       "DIII07","DIII08","DIII09","DIII10","DIII11"]]);
In [128]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["AVR00","AVR01","AVR02","AVR03","AVR04","AVR05",
                       "AVR06","AVR07","AVR08","AVR09","AVR10","AVR11"]]);
In [129]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["AVL00","AVL01","AVL02","AVL03","AVL04","AVL05","AVL06","AVL07","AVL08","AVL09","AVL10","AVL11"]]);
In [130]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["AVF00","AVF01","AVF02","AVF03","AVF04","AVF05","AVF06","AVF07","AVF08","AVF09","AVF10","AVF11"]]);
In [131]:
sns.set(rc={'figure.figsize':(12,6)})
sns.boxplot(data=final_df[["V100","V101","V102","V103","V104","V105","V106","V107","V108","V109","V110","V111"]]);
In [132]:
final_df["V101"].value_counts().sort_index(ascending=False)
Out[132]:
216.0     1
112.0     1
84.0      1
72.0      1
68.0      1
64.0      1
48.0      6
44.0      6
40.0     13
36.0     36
32.0     63
28.0     81
24.0     88
20.0     57
16.0     13
12.0      4
0.0      79
Name: V101, dtype: int64

V101 有一个离群值，但是当我们查看其他集合（V201、V301、V501）时，我们可以看到类似地也有一个离群值。由于我们的数据存在严重偏差，我不能说这些异常值应该被删除。

例如，当我们查看数据时，我们可以看到第 8 类（室上性早搏）只有 2 个实例。或者 3类（心室早搏 (PVC)）只有 3 个。我们图中出现的异常值可能属于这些实例，需要保留。

In [133]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V200","V201","V202","V203","V204","V205","V206","V207","V208","V209","V210","V211"]]);
In [134]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V300","V301","V302","V303","V304","V305","V306","V307","V308","V309","V310","V311"]]);
In [135]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V400","V401","V402","V403","V404","V405","V406","V407","V408","V409","V410","V411"]]);
In [136]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V500","V501","V502","V503","V504","V505","V506","V507","V508","V509","V510","V511"]]);
In [137]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V600","V601","V602","V603","V604","V605","V606","V607","V608","V609","V610","V611"]]);
In [138]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["JJ_Wave","Amp_Q_Wave","Amp_R_Wave","Amp_S_Wave","R_Prime_Wave","S_Prime_Wave","P_Wave","T_Wave"]]);
In [139]:
sns.set(rc={'figure.figsize':(13.7,5.27)})
sns.boxplot(data=final_df[["QRSA","QRSTA","DII170","DII171","DII172","DII173","DII174","DII175","DII176","DII177","DII178","DII179"]]);
In [140]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["DIII180","DIII181","DIII182","DIII183","DIII184","DIII185","DIII186","DIII187","DIII188","DIII189"]]);
In [141]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["AVR190","AVR191","AVR192","AVR193","AVR194","AVR195","AVR196","AVR197","AVR198","AVR199"]]);
In [142]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["AVL200","AVL201","AVL202","AVL203","AVL204","AVL205","AVL206","AVL207","AVL208","AVL209"]]);
In [143]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["AVF210","AVF211","AVF212","AVF213","AVF214","AVF215","AVF216","AVF217","AVF218","AVF219"]]);
In [144]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V1220","V1221","V1222","V1223","V1224","V1225","V1226","V1227","V1228","V1229"]]);
In [145]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V2230","V2231","V2232","V2233","V2234","V2235","V2236","V2237","V2238","V2239"]]);
In [146]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V3240","V3241","V3242","V3243","V3244","V3245","V3246","V3247","V3248","V3249"]]);
In [147]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V4250","V4251","V4252","V4253","V4254","V4255","V4256","V4257","V4258","V4259"]]);
In [148]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V5260","V5261","V5262","V5263","V5264","V5265","V5266","V5267","V5268","V5269"]]);
In [149]:
sns.set(rc={'figure.figsize':(11.7,5.27)})
sns.boxplot(data=final_df[["V6270","V6271","V6272","V6273","V6274","V6275","V6276","V6277","V6278","V6279"]]);

身体状况如何影响人的健康¶

了解人们的年龄是否影响他们的状况¶

In [150]:
class_list = []
j = 1
new_class_names =[]
for i in class_names:
   
    if(i != "1.Degree AtrioVentricular block" and i!= '3.Degree AV block' and i!= "2.Degree AV block"  ):
        class_list.append([j,i,grouped_mean["Age"][j],grouped_median["Age"][j],grouped_mode["Age"][j],gouped_std["Age"][j]])
        new_class_names.append(i)
    j+=1
AgeClass = {
    "AgeAVG": grouped_mean["Age"],"AgeMdian":grouped_median["Age"],
            "AgeMode": grouped_mode["Age"],"AgeStd":gouped_std['Age'],"Class":new_class_names }
AgeClassDF =pd.DataFrame(AgeClass)
In [151]:
fig, ax = plt.subplots(figsize=(15,10))
AgeClassDF.plot(kind="bar",ax=ax)
ax.set_title("Plot of the Age statistics")
ax.set_ylabel('Values of Mean, Median, Mode and Standard Deviation')
ax.set_xlabel('Classes')
fig.tight_layout()
plt.show()
AgeClassDF
Out[151]:
AgeAVG AgeMdian AgeMode AgeStd Class
class
1.0 46.273469 46.0 0.0 14.556092 Normal
2.0 51.750000 55.0 0.0 14.160418 Ischemic changes (CAD)
3.0 53.333333 50.0 0.0 11.286317 Old Anterior Myocardial Infraction
4.0 57.266667 57.0 0.0 11.895177 Old Inferior Myocardial Infraction
5.0 30.846154 34.0 0.0 27.904140 Sinus tachycardy
6.0 47.920000 46.0 0.0 13.165359 Sinus bradycardy
7.0 60.666667 67.0 0.0 18.339393 Ventricular Premature Contraction (PVC)
8.0 71.000000 71.0 0.0 5.656854 Supraventricular Premature Contraction
9.0 58.222222 66.0 0.0 21.211894 Left Boundle branch block
10.0 38.920000 38.5 0.0 17.692198 Right boundle branch block
14.0 19.500000 17.5 0.0 11.090537 Left Ventricule hypertrophy
15.0 57.400000 58.0 0.0 6.188699 Atrial Fibrillation or Flutter
16.0 44.272727 43.5 0.0 19.644900 Others

了解人们的性别是否影响他们的状况¶


男性和女性基本统计

In [152]:
class_list = []
j = 1
new_class_names =[]
for i in class_names:
   
    if(i != "1.Degree AtrioVentricular block" and i!= '3.Degree AV block' and i!= "2.Degree AV block"  ):
        class_list.append([j,i,grouped_mean["Sex"][j],grouped_median["Sex"][j],grouped_mode["Sex"][j],gouped_std["Sex"][j]])
        new_class_names.append(i)
    j+=1
SexClass = {
    "SexAVG": grouped_mean["Sex"],"SexMdian":grouped_median["Sex"],
            "SexMode": grouped_mode["Sex"],"SexStd":gouped_std['Sex'],"Class":new_class_names }
SexClassDF =pd.DataFrame(SexClass)
In [153]:
fig, ax = plt.subplots(figsize=(15,10))
SexClassDF.plot(kind="bar",ax=ax)
ax.set_title("Plot of the Sex statistics")
ax.set_ylabel('Values of Mean, Median, Mode and Standard Deviation')
ax.set_xlabel('Classes')
fig.tight_layout()
plt.show()
SexClassDF
Out[153]:
SexAVG SexMdian SexMode SexStd Class
class
1.0 0.653061 1.0 0.0 0.476970 Normal
2.0 0.590909 1.0 0.0 0.497350 Ischemic changes (CAD)
3.0 0.000000 0.0 0.0 0.000000 Old Anterior Myocardial Infraction
4.0 0.266667 0.0 0.0 0.457738 Old Inferior Myocardial Infraction
5.0 0.692308 1.0 0.0 0.480384 Sinus tachycardy
6.0 0.440000 0.0 0.0 0.506623 Sinus bradycardy
7.0 0.000000 0.0 0.0 0.000000 Ventricular Premature Contraction (PVC)
8.0 0.000000 0.0 0.0 0.000000 Supraventricular Premature Contraction
9.0 0.555556 1.0 0.0 0.527046 Left Boundle branch block
10.0 0.440000 0.0 0.0 0.501427 Right boundle branch block
14.0 0.250000 0.0 0.0 0.500000 Left Ventricule hypertrophy
15.0 0.800000 1.0 0.0 0.447214 Atrial Fibrillation or Flutter
16.0 0.318182 0.0 0.0 0.476731 Others

了解患者的体重是否影响患者的病情¶

In [154]:
class_list = []
j = 1
new_class_names =[]
for i in class_names:
   
    if(i != "1.Degree AtrioVentricular block" and i!= '3.Degree AV block' and i!= "2.Degree AV block"  ):
        class_list.append([j,i,grouped_mean["Weight"][j],grouped_median["Weight"][j],grouped_mode["Weight"][j],gouped_std["Weight"][j]])
        new_class_names.append(i)
    j+=1
WeightClass = {
    "WeightAVG": grouped_mean["Weight"],"WeightMdian":grouped_median["Weight"],
            "WeightMode": grouped_mode["Weight"],"WeightStd":gouped_std['Weight'],"Class":new_class_names }
WeightClassDF =pd.DataFrame(WeightClass)
In [155]:
fig, ax = plt.subplots(figsize=(15,10))
WeightClassDF.plot(kind="bar",ax=ax)
ax.set_title("Plot of the Weights statistics")
ax.set_ylabel('Values of Mean, Median, Mode and Standard Deviation')
ax.set_xlabel('Classes')
fig.tight_layout()
plt.show()
WeightClassDF
Out[155]:
WeightAVG WeightMdian WeightMode WeightStd Class
class
1.0 68.669388 68.0 0.0 14.454595 Normal
2.0 73.363636 70.0 0.0 20.617323 Ischemic changes (CAD)
3.0 76.866667 74.0 0.0 12.397388 Old Anterior Myocardial Infraction
4.0 73.866667 76.0 0.0 9.318696 Old Inferior Myocardial Infraction
5.0 44.615385 51.0 0.0 29.259011 Sinus tachycardy
6.0 68.000000 66.0 0.0 11.045361 Sinus bradycardy
7.0 73.000000 74.0 0.0 7.549834 Ventricular Premature Contraction (PVC)
8.0 79.000000 79.0 0.0 1.414214 Supraventricular Premature Contraction
9.0 66.444444 72.0 0.0 18.808981 Left Boundle branch block
10.0 63.300000 63.5 0.0 16.579124 Right boundle branch block
14.0 50.250000 51.5 0.0 19.653244 Left Ventricule hypertrophy
15.0 76.000000 78.0 0.0 13.209845 Atrial Fibrillation or Flutter
16.0 68.136364 70.5 0.0 18.061523 Others

了解人们的身高是否影响他们的状况¶

In [156]:
class_list = []
j = 1
new_class_names =[]
for i in class_names:
   
    if(i != "1.Degree AtrioVentricular block" and i!= '3.Degree AV block' and i!= "2.Degree AV block"  ):
        class_list.append([j,i,grouped_mean["Height"][j],grouped_median["Height"][j],grouped_mode["Height"][j],gouped_std["Height"][j]])
        new_class_names.append(i)
    j+=1
HeightClass = {
    "HeightAVG": grouped_mean["Height"],"HeightMdian":grouped_median["Height"],
            "HeightMode": grouped_mode["Height"],"HeightStd":gouped_std['Height'],"Class":new_class_names }
HeightClassDF =pd.DataFrame(HeightClass)
In [157]:
fig, ax = plt.subplots(figsize=(15,10))
HeightClassDF.plot(kind="bar",ax=ax)
ax.set_title("Plot of the Heights statistics")
ax.set_ylabel('Values of Mean, Median, Mode and Standard Deviation')
ax.set_xlabel('Classes')
fig.tight_layout()
plt.show()
HeightClassDF
Out[157]:
HeightAVG HeightMdian HeightMode HeightStd Class
class
1.0 164.102041 163.0 0.0 8.048126 Normal
2.0 163.068182 160.5 0.0 7.456534 Ischemic changes (CAD)
3.0 170.600000 170.0 0.0 4.579457 Old Anterior Myocardial Infraction
4.0 166.000000 165.0 0.0 7.973169 Old Inferior Myocardial Infraction
5.0 233.615385 160.0 0.0 208.500015 Sinus tachycardy
6.0 165.040000 165.0 0.0 6.996904 Sinus bradycardy
7.0 178.000000 176.0 0.0 11.135529 Ventricular Premature Contraction (PVC)
8.0 176.500000 176.5 0.0 19.091883 Supraventricular Premature Contraction
9.0 155.666667 158.0 0.0 16.209565 Left Boundle branch block
10.0 163.520000 165.0 0.0 13.699754 Right boundle branch block
14.0 156.000000 167.0 0.0 24.097026 Left Ventricule hypertrophy
15.0 161.400000 160.0 0.0 4.098780 Atrial Fibrillation or Flutter
16.0 165.000000 168.0 0.0 13.288018 Others

了解 QRS 持续时间是否影响人体状况¶

In [158]:
class_list = []
j = 1
new_class_names =[]
for i in class_names:
   
    if(i != "1.Degree AtrioVentricular block" and i!= '3.Degree AV block' and i!= "2.Degree AV block"  ):
        class_list.append([j,i,grouped_mean["QRS_Dur"][j],grouped_median["QRS_Dur"][j],grouped_mode["QRS_Dur"][j],gouped_std["QRS_Dur"][j]])
        new_class_names.append(i)
    j+=1
QRSTClass = {
    "QRSDAVG": grouped_mean["QRS_Dur"],"QRSDMdian":grouped_median["QRS_Dur"],
            "QRSDMode": grouped_mode["QRS_Dur"],"QRSDStd":gouped_std['QRS_Dur'],"Class":new_class_names }
QRSTClassDF =pd.DataFrame(QRSTClass)
In [159]:
fig, ax = plt.subplots(figsize=(15,10))
QRSTClassDF.plot(kind="bar",ax=ax)
ax.set_title("Plot of the QRS Duration  statistics")
ax.set_ylabel('Values of Mean, Median, Mode and Standard Deviation')
ax.set_xlabel('Classes')
fig.tight_layout()
plt.show()
QRSTClassDF
Out[159]:
QRSDAVG QRSDMdian QRSDMode QRSDStd Class
class
1.0 84.277551 84.0 0.0 8.735439 Normal
2.0 89.818182 90.5 0.0 10.207778 Ischemic changes (CAD)
3.0 93.333333 93.0 0.0 9.131317 Old Anterior Myocardial Infraction
4.0 89.866667 93.0 0.0 10.979635 Old Inferior Myocardial Infraction
5.0 88.000000 82.0 0.0 29.796532 Sinus tachycardy
6.0 83.160000 83.0 0.0 8.561931 Sinus bradycardy
7.0 96.333333 92.0 0.0 11.150486 Ventricular Premature Contraction (PVC)
8.0 98.500000 98.5 0.0 10.606602 Supraventricular Premature Contraction
9.0 154.444444 147.0 0.0 16.409685 Left Boundle branch block
10.0 97.580000 95.0 0.0 15.129495 Right boundle branch block
14.0 99.250000 97.5 0.0 6.396614 Left Ventricule hypertrophy
15.0 88.400000 82.0 0.0 19.034180 Atrial Fibrillation or Flutter
16.0 92.136364 94.0 0.0 11.825298 Others

分布图¶



我们研究了上面分析的特征的分布和联合分布


我们用经验分布来拟合特征分布

In [161]:
sns.jointplot(x="Sex", y="Age", data=final_df, kind='kde')
Out[161]:
<seaborn.axisgrid.JointGrid at 0x1cc0210b050>
In [165]:
sns.jointplot(x="Sex", y="Weight", data=final_df, kind='resid')
Out[165]:
<seaborn.axisgrid.JointGrid at 0x1cc0ed8b350>
In [166]:
sns.jointplot(x="Age", y="Weight", data=final_df, kind='scatter')
Out[166]:
<seaborn.axisgrid.JointGrid at 0x1cc0ffbe1d0>
In [167]:
sns.jointplot(x="QRS", y="Age", data=final_df, kind='reg',color="green")
Out[167]:
<seaborn.axisgrid.JointGrid at 0x1cc10398b90>
In [168]:
sns.jointplot(x="QRS", y="Sex", data=final_df, kind='reg',color="navy")
Out[168]:
<seaborn.axisgrid.JointGrid at 0x1cc10719ad0>
In [169]:
sns.jointplot(x="QRS", y="Weight", data=final_df, kind='reg',color="brown")
Out[169]:
<seaborn.axisgrid.JointGrid at 0x1cc10cdd190>
In [174]:
g = sns.JointGrid(x="Age", y="Weight", data=final_df) 
g.plot_joint(sns.regplot, order=2) 
g.plot_marginals(sns.histplot,color="red")
Out[174]:
<seaborn.axisgrid.JointGrid at 0x1cc11dad190>
In [175]:
g = sns.JointGrid(x="Weight", y="QRS", data=final_df) 
g.plot_joint(sns.regplot, order=2) 
g.plot_marginals(sns.histplot)
Out[175]:
<seaborn.axisgrid.JointGrid at 0x1cc11844d90>
In [176]:
g = sns.JointGrid(x="Age", y="QRS", data=final_df) 
g.plot_joint(sns.regplot, order=2) 
g.plot_marginals(sns.histplot)
Out[176]:
<seaborn.axisgrid.JointGrid at 0x1cc1267c110>