机器学习 预测模型_使用机器学习模型预测心力衰竭的生存时间-第一部分
機(jī)器學(xué)習(xí) 預(yù)測(cè)模型
數(shù)據(jù)科學(xué) ， 機(jī)器學(xué)習(xí) (Data Science, Machine Learning)
前言 (Preface)
Cardiovascular diseases are diseases of the heart and blood vessels and they typically include heart attacks, strokes, and heart failures [1]. According to the World Health Organization (WHO), cardiovascular diseases like ischaemic heart disease and stroke have been the leading causes of deaths worldwide for the last decade and a half [2].
心血管疾病是心臟和血管疾病，通常包括心臟病發(fā)作，中風(fēng)和心力衰竭[1]。 根據(jù)世界衛(wèi)生組織(WHO)的研究，在過(guò)去的15年中，缺血性心臟病和中風(fēng)等心血管疾病已成為全球死亡的主要原因[2]。
動(dòng)機(jī) (Motivation)
A few months ago, a new heart failure dataset was uploaded on Kaggle. This dataset contained health records of 299 anonymized patients and had 12 clinical and lifestyle features. The task was to predict heart failure using these features.
幾個(gè)月前，一個(gè)新的心力衰竭數(shù)據(jù)集被上傳到Kaggle上 。 該數(shù)據(jù)集包含299名匿名患者的健康記錄，并具有12種臨床和生活方式特征。 他們的任務(wù)是使用這些功能來(lái)預(yù)測(cè)心力衰竭。
Through this post, I aim to document my workflow on this task and present it as a research exercise. So this would naturally involve a bit of domain knowledge, references to journal papers, and deriving insights from them.
通過(guò)這篇文章，我旨在記錄我有關(guān)此任務(wù)的工作流程，并將其作為研究練習(xí)進(jìn)行介紹。 因此，這自然會(huì)涉及到一些領(lǐng)域知識(shí)，對(duì)期刊論文的引用以及從中得出的見解。
Warning: This post is nearly 10 minutes long and things may get a little dense as you scroll down, but I encourage you to give it a shot.
警告：這篇文章將近10分鐘，當(dāng)您向下滾動(dòng)時(shí)，內(nèi)容可能會(huì)變得有些密集，但我建議您試一試。
關(guān)于數(shù)據(jù) (About the data)
The dataset was originally released by Ahmed et al., in 2017 [3] as a supplement to their analysis of survival of heart failure patients at Faisalabad Institute of Cardiology and at the Allied Hospital in Faisalabad, Pakistan. The dataset was subsequently accessed and analyzed by Chicco and Jurman in 2020 to predict heart failures using a bunch of machine learning techniques [4]. The dataset hosted on Kaggle cites these authors and their research paper.
該數(shù)據(jù)集最初由Ahmed等人在2017年發(fā)布[3]，作為他們對(duì)巴基斯坦費(fèi)薩拉巴德心臟病研究所和聯(lián)合王國(guó)費(fèi)薩拉巴德聯(lián)合醫(yī)院心力衰竭患者生存率分析的補(bǔ)充。 隨后，Chicco和Jurman于2020年訪問并分析了該數(shù)據(jù)集，以使用一系列機(jī)器學(xué)習(xí)技術(shù)預(yù)測(cè)心力衰竭[4]。 Kaggle托管的數(shù)據(jù)集引用了這些作者及其研究論文。
The dataset primarily consists of clinical and lifestyle features of 105 female and 194 male heart failure patients. You can find each feature explained in the figure below.
該數(shù)據(jù)集主要由105位女性和194位男性心力衰竭患者的臨床和生活方式特征組成。 您可以找到下圖中說(shuō)明的每個(gè)功能。
Fig. 1 — Clinical and lifestyle features of 299 patients in the dataset (credit: author)圖1 —數(shù)據(jù)集中299名患者的臨床和生活方式特征(來(lái)源：作者)項(xiàng)目工作流程 (Project Workflow)
The workflow would be pretty straightforward —
工作流程將非常簡(jiǎn)單-
Data Preprocessing — Cleaning the data, imputing missing values, creating new features if needed, etc.
數(shù)據(jù)預(yù)處理-清理數(shù)據(jù)，估算缺失值，根據(jù)需要?jiǎng)?chuàng)建新功能等。
Exploratory Data Analysis — This would involve summary statistics, plotting relationships, mapping trends, etc.
探索性數(shù)據(jù)分析-這將涉及摘要統(tǒng)計(jì)，繪制關(guān)系，繪制趨勢(shì)等。
Model Building — Building a baseline prediction model, followed by at least 2 classification models to train and test.
建立模型—建立基線預(yù)測(cè)模型，然后建立至少兩個(gè)分類模型以進(jìn)行訓(xùn)練和測(cè)試。
Hyper-parameter Tuning — Fine-tune the hyper-parameters of each model to arrive at acceptable levels of prediction metrics.
超參數(shù)調(diào)整-微調(diào)每個(gè)模型的超參數(shù)，以達(dá)到可接受的預(yù)測(cè)指標(biāo)水平。
Consolidating Results — Presenting relevant findings in a clear and concise manner.
合并結(jié)果—清晰，簡(jiǎn)明地陳述相關(guān)發(fā)現(xiàn)。
The entire project can be found as a Jupyter notebook on my GitHub repository.
整個(gè)項(xiàng)目都可以在我的 GitHub 存儲(chǔ)庫(kù)中 找到，作為Jupyter筆記本 。
讓我們開始！ (Let’s begin!)
數(shù)據(jù)預(yù)處理 (Data Preprocessing)
Let’s read in the .csv file into a dataframe —
讓我們將.csv文件讀入數(shù)據(jù)框-
df = pd.read_csv('heart_failure_clinical_records_dataset.csv')df.info() is a quick way to get a summary of the dataframe data types. We see that the dataset has no missing or spurious values and is clean enough to begin data exploration.
df.info()是獲取數(shù)據(jù)框數(shù)據(jù)類型摘要的快速方法。 我們看到數(shù)據(jù)集沒有丟失或偽造的值，并且足夠干凈以開始數(shù)據(jù)探索。
<class 'pandas.core.frame.DataFrame'>RangeIndex: 299 entries, 0 to 298
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 age 299 non-null float64
1 anaemia 299 non-null int64
2 creatinine_phosphokinase 299 non-null int64
3 diabetes 299 non-null int64
4 ejection_fraction 299 non-null int64
5 high_blood_pressure 299 non-null int64
6 platelets 299 non-null float64
7 serum_creatinine 299 non-null float64
8 serum_sodium 299 non-null int64
9 sex 299 non-null int64
10 smoking 299 non-null int64
11 time 299 non-null int64
12 DEATH_EVENT 299 non-null int64
dtypes: float64(3), int64(10)
memory usage: 30.5 KB
But before that, let us rearrange and rename some of the features, add another feature called chk(which would be useful later during EDA) and replace the binary values in the categorical features with their labels (again, useful during EDA).
但是在此之前，讓我們重新排列并重命名一些功能，添加另一個(gè)名為chk功能( 在EDA中稍后會(huì) chk )，然后用其標(biāo)簽替換分類功能中的二進(jìn)制值( 再次在EDA中使用 )。
df = df.rename(columns={'smoking':'smk','diabetes':'dia',
'anaemia':'anm',
'platelets':'plt',
'high_blood_pressure':'hbp',
'creatinine_phosphokinase':'cpk',
'ejection_fraction':'ejf',
'serum_creatinine':'scr',
'serum_sodium':'sna',
'DEATH_EVENT':'death'})df['chk'] = 1df['sex'] = df['sex'].apply(lambda x: 'Female' if x==0 else 'Male')
df['smk'] = df['smk'].apply(lambda x: 'No' if x==0 else 'Yes')
df['dia'] = df['dia'].apply(lambda x: 'No' if x==0 else 'Yes')
df['anm'] = df['anm'].apply(lambda x: 'No' if x==0 else 'Yes')
df['hbp'] = df['hbp'].apply(lambda x: 'No' if x==0 else 'Yes')
df['death'] = df['death'].apply(lambda x: 'No' if x==0 else 'Yes')df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 299 entries, 0 to 298
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 sex 299 non-null object
1 age 299 non-null float64
2 smk 299 non-null object
3 dia 299 non-null object
4 hbp 299 non-null object
5 anm 299 non-null object
6 plt 299 non-null float64
7 ejf 299 non-null int64
8 cpk 299 non-null int64
9 scr 299 non-null float64
10 sna 299 non-null int64
11 time 299 non-null int64
12 death 299 non-null object
13 chk 299 non-null int64
dtypes: float64(3), int64(5), object(6)
memory usage: 32.8+ KB
We observe that sex, dia, anm, hbp, smk anddeathare categorical features (object), while age, plt,cpk, ejf, scr, timeand sna are numerical features (int64 or float64). All features except death would be potential predictors and death would be the target for our prospective ML model.
我們觀察到， sex ， dia ， anm ， hbp ， smk和death的類別特征( 對(duì)象 )，而age ， plt ， cpk ， ejf ， scr ， time和sna的數(shù)字功能(Int64的或float64)。 除death以外的所有功能都是潛在的預(yù)測(cè)因素，而death將成為我們預(yù)期的ML模型的目標(biāo)。
探索性數(shù)據(jù)分析 (Exploratory Data Analysis)
A.數(shù)值特征匯總統(tǒng)計(jì) (A. Summary Statistics of Numerical Features)
Since our dataset has many numerical features, it would be helpful to look at some aggregate measures of the data in hand, with the help of df.describe() (Usually, this method gives values up to 6 decimal places, so it would better to round it off to two by df.describe().round(2))
由于我們的數(shù)據(jù)集具有許多數(shù)值特征，因此在df.describe()的幫助下df.describe()手頭數(shù)據(jù)的一些聚合度量將很有幫助( 通常，此方法最多可提供小數(shù)點(diǎn)后6位的值，因此最好到輪它關(guān)閉兩個(gè)由 df.describe().round(2)
Age: We can see that the average age of the patients is 60 years with most of the patients (<75%) below 70 years and above 40 years. The follow-up time after their heart failure also varies from 4 days to 285 days, with an average of 130 days.
年齡 ：我們可以看到患者的平均年齡為60歲，其中大多數(shù)患者(<75％)低于70歲且高于40歲。 他們心力衰竭后的隨訪時(shí)間也從4天到285天不等，平均為130天。
Platelets: These are a type of blood cells that are responsible for repairing damaged blood vessels. A normal person has a platelet count of 150,000–400,000 kiloplatelets/mL of blood [5]. In our dataset, 75% of the patients have a platelet count well within this range.
血小板 ： 血小板 是負(fù)責(zé)修復(fù)受損血管的一種血細(xì)胞。 正常人的血小板計(jì)數(shù)為150,000–400,000血小板/ mL血液[5]。 在我們的數(shù)據(jù)集中，有75％的患者血小板計(jì)數(shù)在此范圍內(nèi)。
Ejection fraction: This is a measure (in %) of how much blood is pumped out of a ventricle in each contraction. To brush up a little human anatomy — the heart has 4 chambers of which the atria receive blood from different parts of the body and the ventricles pump it to back. The left ventricle is the thickest chamber and pumps blood to the rest of the body while the right ventricle pumps blood to the lungs. In a healthy adult, this fraction is 55% and heart failure with reduced ejection fraction implies a value < 40%[6]. In our dataset, 75% of the patients have this value < 45% which is expected because they are all heart failure patients in the first place.
射血分?jǐn)?shù) ： 這是每次收縮中從腦室中抽出多少血液的量度(％)。 為了梳理一點(diǎn)人體解剖學(xué)，心臟有4個(gè)腔室，心房從身體的不同部位接收血液，心室將其泵回。 左心室是最厚的腔室，將血液泵送到身體的其余部分，而右心室則將血液泵到肺。 在健康的成年人中，這一比例為55％，而射血分?jǐn)?shù)降低的心力衰竭意味著其值<40％[6]。 在我們的數(shù)據(jù)集中，有75％的患者的此值<45％ ，這是可以預(yù)期的，因?yàn)樗麄兪紫榷际切牧λソ呋颊摺?
Creatinine Phosphokinase: This is an enzyme that is present in the blood and helps in repairing damaged tissues. A high level of CPK implies heart failure or injury. The normal levels in males are 55–170 mcg/L and in females are 30–135 mcg/L [7]. In our dataset, since all patients have had heart failure, the average value (550 mcg/L) and median (250 mcg/L) are higher than normal.
肌酐磷酸激酶 ： 這是一種存在于血液中的酶，有助于修復(fù)受損的組織。 高水平的CPK意味著心力衰竭或傷害。 男性的正常水平為55–170 mcg / L，女性為30–135 mcg / L [7]。 在我們的數(shù)據(jù)集中，由于所有患者都患有心力衰竭， 因此平均值(550 mcg / L)和中位數(shù)(250 mcg / L)高于正常水平。
Serum creatinine: This is a waste product that is produced as a part of muscle metabolism especially during muscle breakdown. This creatinine is filtered by the kidneys and increased levels are indicative of poor cardiac output and possible renal failure[8]. The normal levels are between 0.84 to 1.21 mg/dL [9] and in our dataset, the average and median are above 1.10 mg/dL, which is pretty close to the upper limit of the normal range.
血清肌酐 ： 這是一種廢物，是肌肉代謝的一部分，特別是在肌肉分解過(guò)程中。 肌酐被腎臟過(guò)濾，水平升高表明心輸出量不良和可能的腎衰竭 [8]。 正常水平在0.84至1.21 mg / dL之間[9]，在我們的數(shù)據(jù)集中，平均值和中位數(shù)高于1.10 mg / dL， 非常接近正常范圍的上限 。
Serum sodium: This refers to the level of sodium in the blood and a high level of > 135 mEq/L is called hypernatremia, which is considered typical in heart failure patients [10]. In our dataset, we find that the average and the median are > 135 mEq/L.
血清鈉 ： 指血液中的鈉水平，> 135 mEq / L的高水平被稱為高鈉血癥，在心力衰竭患者中被認(rèn)為是典型的 [10]。 在我們的數(shù)據(jù)集中，我們發(fā)現(xiàn)平均值和中位數(shù)> 135 mEq / L。
A neat way to visualize these statistics is with a boxenplot which shows the spread and distribution of values (The line in the center is the median and the diamonds at the end are the outliers).
直觀顯示這些統(tǒng)計(jì)數(shù)據(jù)的一種好方法是使用boxenplot ，該boxenplot顯示值的分布和分布( 中間的線是中位數(shù)，而末端的菱形是異常值 )。
fig,ax = plt.subplots(3,2,figsize=[10,10])num_features_set1 = ['age', 'scr', 'sna']
num_features_set2 = ['plt', 'ejf', 'cpk']
for i in range(0,3):
sns.boxenplot(df[num_features_set1[i]], ax=ax[i,0], color='steelblue')
sns.boxenplot(df[num_features_set2[i]], ax=ax[i,1], color='steelblue')Fig. 2 — Visualising the summary statistics for numerical features of the dataset圖2 —可視化數(shù)據(jù)集數(shù)值特征的摘要統(tǒng)計(jì)
B.分類特征摘要統(tǒng)計(jì) (B. Summary Statistics of Categorical Features)
The number of patients belonging to each of the lifestyle categorical features can be summarised with a simple bar plot .
可以通過(guò)簡(jiǎn)單的bar plot總結(jié)屬于每種生活方式分類特征的患者人數(shù)。
fig = plt.subplots(figsize=[10,6])bar1 = df.smk.value_counts().valuesbar2 = df.hbp.value_counts().values
bar3 = df.dia.value_counts().values
bar4 = df.anm.value_counts().values
ticks = np.arange(0,3, 2)
width = 0.3
plt.bar(ticks, bar1, width=width, color='teal', label='smoker')
plt.bar(ticks+width, bar2, width=width, color='darkorange', label='high blood pressure')
plt.bar(ticks+2*width, bar3, width=width, color='limegreen', label='diabetes')
plt.bar(ticks+3*width, bar4, width=width, color='tomato', label='anaemic')plt.xticks(ticks+1.5*width, ['Yes', 'No'])
plt.ylabel('Number of patients')
plt.legend()Fig. 3 — Total number of patients in each lifestyle categorical feature圖3-每種生活方式分類特征中的患者總數(shù)
Additional summaries can be generated using the crosstab function in pandas. An example is shown for the categorical feature smk . The results can be normalized with respect to either the total number of smokers (‘index’) or the total number of deaths (‘columns’). Since our interest is in predicting survival, we normalize with respect to death.
可以使用pandas中的crosstab功能生成其他摘要。 顯示了分類特征smk的示例。 可以根據(jù)吸煙者總數(shù)( “指數(shù)” )或死亡總數(shù)( “列” )對(duì)結(jié)果進(jìn)行標(biāo)準(zhǔn)化。 由于我們的興趣在于預(yù)測(cè)生存，因此我們將死亡歸一化。
pd.crosstab(index=df['smk'], columns=df['death'], values=df['chk'], aggfunc=np.sum, margins=True)pd.crosstab(index=df['smk'], columns=df['death'], values=df['chk'], aggfunc=np.sum, margins=True, normalize='columns').round(2)*100We see that 68% of all heart failure patients did not smoke while 32% did. Of those who died, 69% were non-smokers while 31% were smokers. Of those who survived, 67% were non-smokers and 33% were smokers. At this point, it is difficult to say, conclusively, that heart failure patients who smoked have a greater chance of dying.
我們發(fā)現(xiàn)68％的心力衰竭患者不吸煙，而32％的人吸煙。 在死亡者中 ，不吸煙者占69％，吸煙者占31％。 在幸存者中 ，不吸煙者占67％，吸煙者占33％。 在這一點(diǎn)上，很難說(shuō)得出結(jié)論，吸煙的心力衰竭患者死亡的機(jī)會(huì)更大。
In a similar manner, let’s summarise the rest of the categorical features and normalize the results with respect to deaths.
以類似的方式，讓我們總結(jié)一下其余的分類特征，并就死亡對(duì)結(jié)果進(jìn)行歸一化。
- 65% of the Male and 35% of the Female heart patients died. 65％的男性心臟病患者和35％的女性心臟病患者死亡。
- 48% of the patients who died were anemic while 41% of the patients who survived were anemic as well. 死亡的患者中有48％貧血，而幸存的患者中有41％貧血。
- 42% of the patients who died and 42% who survived were diabetic. 42％的死亡患者和42％的幸存者患有糖尿病。
- 31% of the dead were smokers while 33% of the survivors were smokers. 死者中有31％是吸煙者，而幸存者中有33％是吸煙者。
- 41% of those who died had high blood pressure, while 33% of those who survived had high blood pressure as well. 死者中有41％患有高血壓，而幸存者中有33％患有高血壓。
Based on these statistics, we get a rough idea that the lifestyle features are almost similarly distributed amongst those who died and those who survived. The difference is the greatest in the case of high blood pressure, which could perhaps have a greater influence on the survival of heart patients.
根據(jù)這些統(tǒng)計(jì)數(shù)據(jù)，我們可以粗略了解一下，生活方式特征在死者和幸存者之間的分布幾乎相似。 在高血壓的情況下，差異最大，這可能對(duì)心臟病患者的生存產(chǎn)生更大的影響。
C.探索數(shù)字特征之間的關(guān)系 (C. Exploring relationships between numerical features)
The next step is to visualize the relationship between features. We start with the numerical features by writing a single line of code to plot a pair-wise plotting of features using seaborn’s pairplot—
下一步是可視化要素之間的關(guān)系。 我們從數(shù)字特征開始，編寫一行代碼以使用seaborn的對(duì)圖繪制特征的pairplot ，
sns.pairplot(df[['plt', 'ejf', 'cpk', 'scr', 'sna', 'death']],hue='death', palette='husl', corner=True)Fig. 4— Pair-wise scatterplots between numerical features in the dataset圖4-數(shù)據(jù)集中數(shù)字特征之間的成對(duì)散點(diǎn)圖
We observe a few interesting points —
我們觀察到一些有趣的觀點(diǎn)-
- Most of the patients who died following a heart failure seem to have a lower Ejection Fraction that those who survived. They also seem to have slightly higher levels of Serum Creatinine and Creatine Phosphokinase. They also tend to be on the higher side of 80 years. 死于心力衰竭的大多數(shù)患者的射血分?jǐn)?shù)似乎比那些幸存者低。 他們的血清肌酐和肌酸磷酸激酶水平似乎也略高。 他們也往往處于80年的較高地位。
There are no strong correlations between features and this can be validated by calculating the Spearman R correlation coefficient (We consider the spearman because we are not sure about the population distribution from which the feature values are drawn).
特征之間沒有很強(qiáng)的相關(guān)性，可以通過(guò)計(jì)算Spearman R相關(guān)系數(shù)來(lái)驗(yàn)證( 我們考慮使用spearman，因?yàn)槲覀儾淮_定從中得出特征值的總體分布 )。
As observed, the correlation coefficients are moderately encouraging for age-serum creatinine and serum creatinine-serum sodium. From literature, we see that with age, the serum creatinine content increases [11], which explains their slightly positive relationship. Literature also tells us [12] that the sodium to serum creatinine ratio is high in the case of chronic kidney disease which implies a negative relationship between the two. The slight negative correlation coefficient also implies the prevalence of renal issues in the patients.
如觀察到的， 年齡-血清肌酐和血清肌酐-血清鈉的相關(guān)系數(shù)適度令人鼓舞 。 從文獻(xiàn)中我們看到，隨著年齡的增長(zhǎng)，血清肌酐含量增加[11]，這說(shuō)明了它們之間的正相關(guān)關(guān)系 。 文獻(xiàn)還告訴我們[12]，在慢性腎臟疾病的情況下，鈉與血清肌酐的比例較高，這意味著兩者之間存在負(fù)相關(guān)關(guān)系 。 輕微的負(fù)相關(guān)系數(shù)也意味著患者中腎臟疾病的患病率。
D.探索分類特征之間的關(guān)系 (D. Exploring relationships between categorical features)
One way of relating categorical features is to create a pivot table and pivot about a subset of the features. This would give us the number of values for a particular subset of feature values. For this dataset, let’s look at the lifestyle features — smoking, anemic, high blood pressure, and diabetes.
一種關(guān)聯(lián)分類要素的方法是創(chuàng)建數(shù)據(jù)透視表并圍繞要素的子集進(jìn)行透視。 這將為我們提供特征值特定子集的值數(shù)量。 對(duì)于此數(shù)據(jù)集，讓我們看一下生活方式特征-吸煙，貧血，高血壓和糖尿病。
lifestyle_surv = pd.pivot_table(df.loc[df.death=='No'],values='chk',
columns=['hbp','dia'],
index=['smk','anm'],
aggfunc=np.sum)lifestyle_dead = pd.pivot_table(df.loc[df.death=='Yes'],
values='chk',
columns=['hbp','dia'],
index=['smk','anm'],
aggfunc=np.sum)fig, ax= plt.subplots(1, 2, figsize=[15,6])
sns.heatmap(lifestyle_surv, cmap='Greens', annot=True, ax=ax[0])
ax[0].set_title('Survivors')
sns.heatmap(lifestyle_dead, cmap='Reds', annot=True, ax=ax[1])
ax[1].set_title('Deceased')Fig. 5— Heatmap of the number of patients in each subset of lifestyle features圖5：生活方式特征的每個(gè)子集中的患者數(shù)量熱圖
A few insights can be drawn —
可以得出一些見解-
- A large number of the patients did not smoke, were not anemic and did not suffer from high blood pressure or diabetes. 許多患者不吸煙，沒有貧血，沒有高血壓或糖尿病。
- There were very few patients who had all the four lifestyle features. 具有這四種生活方式特征的患者很少。
- Many of the survivors were either only smokers or only diabetic. 許多幸存者要么只是吸煙者，要么只是糖尿病患者。
- The majority of the deceased had none of the lifestyle features, or at the most were anemic. 死者中大多數(shù)沒有生活方式特征，或者最多是貧血。
- Many of the deceased were anemic and diabetic as well. 許多死者也是貧血和糖尿病患者。
E.探索所有功能之間的關(guān)系 (E. Exploring relationships between all features)
An easy way to combine categorical and numerical features into a single graph is bypassing the categorical feature as a hue input. In this case, we use the binary death feature and plot violin-plots to visualize the relationships across all features.
將分類特征和數(shù)字特征組合到單個(gè)圖形中的一種簡(jiǎn)單方法是繞過(guò)分類特征作為hue輸入。 在這種情況下，我們使用二進(jìn)制death特征并繪制小提琴圖以可視化所有特征之間的關(guān)系。
fig,ax = plt.subplots(6, 5, figsize=[20,22])cat_features = ['sex','smk','anm', 'dia', 'hbp']
num_features = ['age', 'scr', 'sna', 'plt', 'ejf', 'cpk']
for i in range(0,6):
for j in range(0,5):
sns.violinplot(data=df, x=cat_features[j],y=num_features[i], hue='death', split=True, palette='husl',facet_kws={'despine':False}, ax=ax[i,j])
ax[i,j].legend(title='death', loc='upper center')Fig. 6— Violinplots for relating numerical and categorical features in the dataset圖6－用于關(guān)聯(lián)數(shù)據(jù)集中的數(shù)字和分類特征的Violinplots
Here are a few insights from these plots —
以下是這些圖的一些見解-
Sex: Of the patients who died, the ejection fraction seems to be lower in males than in females. Also, the creatinine phosphokinase seems to be higher in males than in females.
性別 ：在死亡的患者中，男性的射血分?jǐn)?shù)似乎低于女性。 另外，男性的肌酐磷酸激酶似乎高于女性。
Smoking: A slightly lower ejection fraction was seen in the smokers who died than in the non-smokers who died. The creatinine phosphokinase levels seem to be higher in smokers who survived, than in non-smokers who survived.
吸煙 ：死亡的吸煙者的射血分?jǐn)?shù)比未死亡的非吸煙者略低。 存活的吸煙者的肌酐磷酸激酶水平似乎高于存活的非吸煙者。
Anemia: The anemic patients tend to have lower creatinine phosphokinase levels and higher serum creatinine levels, than non-anemic patients. Among the anemic patients, the ejection fraction is lower in those who died than in those who survived.
貧血 ：與非貧血患者相比，貧血患者的肌酐磷酸激酶水平和血清肌酐水平較高。 在貧血患者中，死亡者的射血分?jǐn)?shù)低于幸存者。
Diabetes: The diabetic patients tend to have lower sodium levels and again, the ejection fraction is lower in those who died than in the survivors.
糖尿病 ：糖尿病患者的鈉水平較低，而且死亡者的射血分?jǐn)?shù)比幸存者低。
High Blood Pressure: The ejection fraction seems to show greater variation in deceased patients with high blood pressure than in deceased patients without high blood pressure.
高血壓 ：高血壓的死者的射血分?jǐn)?shù)似乎比沒有高血壓的死者更大。
I hope you found this useful. The steps for building ML models, tuning their hyper-parameters, and consolidating the results will be shown in the next post.
希望您覺得這有用。 建立ML模型，調(diào)整其超參數(shù)以及合并結(jié)果的步驟將在下一篇文章中顯示。
Ciao!
再見！
翻譯自: https://medium.com/towards-artificial-intelligence/predicting-heart-failure-survival-with-machine-learning-models-part-i-7ff1ab58cff8
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