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AIDS Clinical Study Analysis

Brashon Ford

Local library import

We import all the required local libraries libraries

python jupyter={"outputs_hidden": true} pip install --upgrade --user nbconvert

python jupyter={"outputs_hidden": true} pip install --upgrade jupyter nbconvert

pip install notebook --upgrade 
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Note: you may need to restart the kernel to use updated packages.


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Note: you may need to restart the kernel to use updated packages.

python jupyter={"outputs_hidden": true} pip install -U notebook-as-pdf

!sudo apt-get install texlive texlive-xetex texlive-fonts-recommended texlive-generic-extra texlive-generic-recommended
Password:

python jupyter={"outputs_hidden": true} !pyppeteer-install

python jupyter={"outputs_hidden": true} !jupyter-nbconvert --to PDFviaHTML ProjectAIDS.ipynb

!sudo apt-get install texlive texlive-latex-extra pandoc
Password:
sudo: a password is required

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.pipeline import make_pipeline
from scipy.spatial import distance_matrix
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
import numpy as np
pip install ucimlrepo
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Note: you may need to restart the kernel to use updated packages.

from ucimlrepo import fetch_ucirepo 
  
# fetch dataset 
aids_clinical_trials_group_study_175 = fetch_ucirepo(id=890) 
  
# data (as pandas dataframes) 
X = pd.DataFrame(aids_clinical_trials_group_study_175.data.features)
y = pd.DataFrame(aids_clinical_trials_group_study_175.data.targets)
  
# metadata 
print(aids_clinical_trials_group_study_175.metadata) 
  
# variable information 
print(aids_clinical_trials_group_study_175.variables) 
{'uci_id': 890, 'name': 'AIDS Clinical Trials Group Study 175', 'repository_url': 'https://archive.ics.uci.edu/dataset/890/aids+clinical+trials+group+study+175', 'data_url': 'https://archive.ics.uci.edu/static/public/890/data.csv', 'abstract': 'The AIDS Clinical Trials Group Study 175 Dataset contains healthcare statistics and categorical information about patients who have been diagnosed with AIDS. This dataset was initially published in 1996. The prediction task is to predict whether or not each patient died within a certain window of time or not. ', 'area': 'Health and Medicine', 'tasks': ['Classification', 'Regression'], 'characteristics': ['Tabular', 'Multivariate'], 'num_instances': 2139, 'num_features': 23, 'feature_types': ['Categorical', 'Integer'], 'demographics': ['Age', 'Sexual Orientation', 'Race', 'Gender'], 'target_col': ['cid'], 'index_col': ['pidnum'], 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1996, 'last_updated': 'Fri Nov 03 2023', 'dataset_doi': '10.24432/C5ZG8F', 'creators': ['S. Hammer', 'D. Katzenstein', 'M. Hughes', 'H. Gundacker', 'R. Schooley', 'R. Haubrich', 'W. K.', 'M. Lederman', 'J. Phair', 'M. Niu', 'M. Hirsch', 'T. Merigan'], 'intro_paper': {'title': 'A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. AIDS Clinical Trials Group Study 175 Study Team.', 'authors': 'S. Hammer, D. Katzenstein, M. Hughes, H. Gundacker, R. Schooley, R. Haubrich, W. K. Henry, M. Lederman, J. Phair, M. Niu, M. Hirsch, T. Merigan', 'published_in': 'New England Journal of Medicine', 'year': 1996, 'url': 'https://www.semanticscholar.org/paper/c7c401dd7d49ead07e70b299e422b27314589d2f', 'doi': None}, 'additional_info': {'summary': None, 'purpose': 'To examine the performance of two different types of AIDS treatments', 'funded_by': '- AIDS Clinical Trials Group of the National Institute of Allergy and Infectious Diseases\n- General Research Center units funded by the National Center for Research Resources', 'instances_represent': '- Health records\n- AIDS patients\n- US only', 'recommended_data_splits': 'Cross validation or a single train-test split could be used.', 'sensitive_data': '- Ethnicity (race)\n- Gender', 'preprocessing_description': 'No', 'variable_info': '- Personal information (age, weight, race, gender, sexual activity)\n- Medical history (hemophilia, history of IV drugs)\n- Treatment history (ZDV/non-ZDV treatment history)\n- Lab results (CD4/CD8 counts)', 'citation': None}, 'external_url': 'https://classic.clinicaltrials.gov/ct2/show/NCT00000625'}
       name     role        type         demographic  \
0    pidnum       ID     Integer                None   
1       cid   Target      Binary                None   
2      time  Feature     Integer                None   
3       trt  Feature     Integer                None   
4       age  Feature     Integer                 Age   
5      wtkg  Feature  Continuous                None   
6      hemo  Feature      Binary                None   
7      homo  Feature      Binary  Sexual Orientation   
8     drugs  Feature      Binary                None   
9    karnof  Feature     Integer                None   
10   oprior  Feature      Binary                None   
11      z30  Feature      Binary                None   
12   zprior  Feature      Binary                None   
13  preanti  Feature     Integer                None   
14     race  Feature     Integer                Race   
15   gender  Feature      Binary              Gender   
16     str2  Feature      Binary                None   
17    strat  Feature     Integer                None   
18  symptom  Feature      Binary                None   
19    treat  Feature      Binary                None   
20   offtrt  Feature      Binary                None   
21     cd40  Feature     Integer                None   
22    cd420  Feature     Integer                None   
23     cd80  Feature     Integer                None   
24    cd820  Feature     Integer                None   

                                          description units missing_values  
0                                          Patient ID  None             no  
1    censoring indicator (1 = failure, 0 = censoring)  None             no  
2                        time to failure or censoring  None             no  
3   treatment indicator (0 = ZDV only; 1 = ZDV + d...  None             no  
4                               age (yrs) at baseline  None             no  
5                             weight (kg) at baseline  None             no  
6                            hemophilia (0=no, 1=yes)  None             no  
7                   homosexual activity (0=no, 1=yes)  None             no  
8                history of IV drug use (0=no, 1=yes)  None             no  
9               Karnofsky score (on a scale of 0-100)  None             no  
10  Non-ZDV antiretroviral therapy pre-175 (0=no, ...  None             no  
11      ZDV in the 30 days prior to 175 (0=no, 1=yes)  None             no  
12                     ZDV prior to 175 (0=no, 1=yes)  None             no  
13             # days pre-175 anti-retroviral therapy  None             no  
14                        race (0=White, 1=non-white)  None             no  
15                                  gender (0=F, 1=M)  None             no  
16    antiretroviral history (0=naive, 1=experienced)  None             no  
17  antiretroviral history stratification (1='Anti...  None             no  
18            symptomatic indicator (0=asymp, 1=symp)  None             no  
19         treatment indicator (0=ZDV only, 1=others)  None             no  
20  indicator of off-trt before 96+/-5 weeks (0=no...  None             no  
21                                    CD4 at baseline  None             no  
22                                CD4 at 20+/-5 weeks  None             no  
23                                    CD8 at baseline  None             no  
24                                CD8 at 20+/-5 weeks  None             no  

X.columns
Index(['time', 'trt', 'age', 'wtkg', 'hemo', 'homo', 'drugs', 'karnof',
       'oprior', 'z30', 'zprior', 'preanti', 'race', 'gender', 'str2', 'strat',
       'symptom', 'treat', 'offtrt', 'cd40', 'cd420', 'cd80', 'cd820'],
      dtype='object')

y
0       0
1       1
2       0
3       0
4       0
       ..
2134    0
2135    0
2136    0
2137    1
2138    0
Name: cid, Length: 2139, dtype: int64

import statsmodels.api as sm


y = combined_data['cid']

X_with_intercept = sm.add_constant(X)


model = sm.OLS(y, X_with_intercept, missing='drop')

# Fit the model to the data
results = model.fit()

# Print the results summary
results.summary()
OLS Regression Results
Dep. Variable: cid R-squared: 0.432
Model: OLS Adj. R-squared: 0.426
Method: Least Squares F-statistic: 73.05
Date: Sun, 04 Feb 2024 Prob (F-statistic): 2.85e-240
Time: 09:47:11 Log-Likelihood: -621.79
No. Observations: 2139 AIC: 1290.
Df Residuals: 2116 BIC: 1420.
Df Model: 22
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
time -0.0009 2.91e-05 -31.723 0.000 -0.001 -0.001
trt -0.0012 0.010 -0.125 0.901 -0.021 0.018
age 0.0017 0.001 2.015 0.044 4.63e-05 0.003
wtkg 0.0004 0.001 0.650 0.516 -0.001 0.001
hemo -0.0445 0.034 -1.316 0.188 -0.111 0.022
homo 0.0124 0.025 0.503 0.615 -0.036 0.061
drugs -0.0497 0.022 -2.251 0.025 -0.093 -0.006
karnof -0.0028 0.001 -2.253 0.024 -0.005 -0.000
oprior 0.0501 0.053 0.954 0.340 -0.053 0.153
z30 0.0730 0.036 2.035 0.042 0.003 0.143
zprior 1.3891 0.136 10.231 0.000 1.123 1.655
preanti 4.741e-05 2.98e-05 1.593 0.111 -1.1e-05 0.000
race -0.0633 0.017 -3.718 0.000 -0.097 -0.030
gender 0.0081 0.028 0.293 0.770 -0.046 0.063
str2 -0.0412 0.049 -0.844 0.399 -0.137 0.055
strat 0.0079 0.029 0.278 0.781 -0.048 0.064
symptom 0.0483 0.019 2.513 0.012 0.011 0.086
treat -0.0192 0.027 -0.726 0.468 -0.071 0.033
offtrt -0.2077 0.017 -12.286 0.000 -0.241 -0.175
cd40 5.066e-05 8.25e-05 0.614 0.539 -0.000 0.000
cd420 -0.0005 7.32e-05 -6.703 0.000 -0.001 -0.000
cd80 -8.767e-06 2.55e-05 -0.344 0.731 -5.87e-05 4.12e-05
cd820 6.601e-05 2.75e-05 2.401 0.016 1.21e-05 0.000
Omnibus: 168.735 Durbin-Watson: 1.969
Prob(Omnibus): 0.000 Jarque-Bera (JB): 237.811
Skew: 0.643 Prob(JB): 2.29e-52
Kurtosis: 4.007 Cond. No. 3.53e+04


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 3.53e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

Regression

combined_data.head()
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time trt age wtkg hemo homo drugs karnof oprior z30 ... str2 strat symptom treat offtrt cd40 cd420 cd80 cd820 cid
0 948 2 48 89.8128 0 0 0 100 0 0 ... 0 1 0 1 0 422 477 566 324 0
1 1002 3 61 49.4424 0 0 0 90 0 1 ... 1 3 0 1 0 162 218 392 564 1
2 961 3 45 88.4520 0 1 1 90 0 1 ... 1 3 0 1 1 326 274 2063 1893 0
3 1166 3 47 85.2768 0 1 0 100 0 1 ... 1 3 0 1 0 287 394 1590 966 0
4 1090 0 43 66.6792 0 1 0 100 0 1 ... 1 3 0 0 0 504 353 870 782 0

5 rows × 24 columns

X_train, X_test, y_train, y_test = train_test_split(combined_data.drop('cid', axis=1), combined_data['cid'], test_size=0.2, random_state=42)
model = LinearRegression()

# Fit the model to the training data
model.fit(X_train, y_train)

# Predict the target variable on the test set
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')
Mean Squared Error: 0.10882556678401331

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)


y_pred = model.predict(X_test)


mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')
Mean Squared Error: 0.10882556678401331

model = sm.OLS(combined_data['cid'], sm.add_constant(combined_data['cd420'])).fit()
model.summary()
OLS Regression Results
Dep. Variable: cid R-squared: 0.120
Model: OLS Adj. R-squared: 0.119
Method: Least Squares F-statistic: 290.4
Date: Sun, 04 Feb 2024 Prob (F-statistic): 3.63e-61
Time: 09:56:11 Log-Likelihood: -1089.8
No. Observations: 2139 AIC: 2184.
Df Residuals: 2137 BIC: 2195.
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
const 0.6248 0.024 26.026 0.000 0.578 0.672
cd420 -0.0010 6.02e-05 -17.043 0.000 -0.001 -0.001
Omnibus: 295.800 Durbin-Watson: 2.033
Prob(Omnibus): 0.000 Jarque-Bera (JB): 369.141
Skew: 0.983 Prob(JB): 6.95e-81
Kurtosis: 2.470 Cond. No. 1.10e+03


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.1e+03. This might indicate that there are
strong multicollinearity or other numerical problems. 
import statsmodels.api as sm

# Assuming 'cid' is the dependent variable
y = combined_data['cid']


independent_vars = ['age','hemo', 'homo', 'drugs', 'karnof', 'cd40', 'cd420']

# Using all specified columns from X as independent variables
X_with_intercept = sm.add_constant(X[independent_vars])

# Create the OLS model object
model = sm.OLS(y, X_with_intercept, missing='drop')

# Fit the model to the data
results = model.fit()

# Print the results summary
results.summary()
OLS Regression Results
Dep. Variable: cid R-squared: 0.132
Model: OLS Adj. R-squared: 0.129
Method: Least Squares F-statistic: 46.29
Date: Sun, 04 Feb 2024 Prob (F-statistic): 2.31e-61
Time: 10:06:44 Log-Likelihood: -1074.7
No. Observations: 2139 AIC: 2165.
Df Residuals: 2131 BIC: 2211.
Df Model: 7
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
const 0.9761 0.151 6.453 0.000 0.679 1.273
age 0.0022 0.001 2.131 0.033 0.000 0.004
hemo -0.0127 0.035 -0.362 0.718 -0.082 0.056
homo 0.0348 0.021 1.680 0.093 -0.006 0.075
drugs -0.0598 0.027 -2.223 0.026 -0.113 -0.007
karnof -0.0049 0.001 -3.299 0.001 -0.008 -0.002
cd40 0.0001 9e-05 1.114 0.265 -7.63e-05 0.000
cd420 -0.0011 7.42e-05 -14.171 0.000 -0.001 -0.001
Omnibus: 283.920 Durbin-Watson: 2.049
Prob(Omnibus): 0.000 Jarque-Bera (JB): 357.990
Skew: 0.971 Prob(JB): 1.83e-78
Kurtosis: 2.501 Cond. No. 9.53e+03


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 9.53e+03. This might indicate that there are
strong multicollinearity or other numerical problems.

python jupyter={"outputs_hidden": true} summary_statistics = X.describe() summary_statistics

python jupyter={"outputs_hidden": true} sns.regplot(x = 'age', y = 'cid', data = combined_data)

Classification Of Dataset

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from scipy.spatial import distance_matrix
from sklearn.cluster import KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

# Extract numerical columns
numerical_columns = combined_data.select_dtypes(include='number').columns

# Separate numerical data
numerical_data = combined_data[numerical_columns]

# Initialize the scaler
scaler = StandardScaler()

# Fit and transform the numerical data
scaled_numerical_data = scaler.fit_transform(numerical_data)

# Create a dataframe with scaled numerical data
X_scaled = pd.DataFrame(scaled_numerical_data, columns=numerical_columns)

# Display the first few rows of the scaled numerical dataframe
X_scaled
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
time trt age wtkg hemo homo drugs karnof oprior z30 ... str2 strat symptom treat offtrt cd40 cd420 cd80 cd820 cid
0 0.235799 0.424960 1.464542 1.107649 -0.303123 -1.396547 -0.388893 0.771836 -0.149888 -1.106116 ... -1.189210 -1.090177 -0.457338 0.575371 -0.754541 0.603131 0.730927 -0.876151 -1.374260 -0.567453
1 0.420600 1.311779 2.957595 -1.936862 -0.303123 -1.396547 -0.388893 -0.923192 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 -0.754541 -1.590108 -1.060207 -1.238586 -0.834779 1.762262
2 0.280288 1.311779 1.119991 1.005025 -0.303123 0.716052 2.571400 -0.923192 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 1.325309 -0.206680 -0.672935 2.242044 2.152597 -0.567453
3 0.981848 1.311779 1.349692 0.765569 -0.303123 0.716052 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 -0.754541 -0.535666 0.156934 1.256802 0.068852 -0.567453
4 0.721757 -1.348678 0.890291 -0.636959 -0.303123 0.716052 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 -1.738009 -0.754541 1.294845 -0.126605 -0.242930 -0.344750 -0.567453
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2134 0.725180 1.311779 -1.636415 -1.646094 3.298990 -1.396547 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 1.325309 -1.674463 -1.814005 -0.886566 -0.484116 -0.567453
2135 -1.656702 -1.348678 -2.095816 2.099680 3.298990 -1.396547 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 -1.738009 1.325309 0.189790 -1.060207 1.608823 0.212713 -0.567453
2136 0.769669 0.424960 2.038793 -0.397503 3.298990 0.716052 -0.388893 -0.923192 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 -0.754541 0.577824 -0.050533 0.842293 0.237440 -0.567453
2137 -1.417145 -1.348678 -2.440366 -1.140667 3.298990 -1.396547 -0.388893 0.771836 -0.149888 -1.106116 ... -1.189210 -1.090177 -0.457338 -1.738009 -0.754541 -1.556366 -1.399071 0.025772 2.028966 1.762262
2138 0.567756 1.311779 1.119991 0.164003 3.298990 -1.396547 -0.388893 0.771836 -0.149888 -1.106116 ... -1.189210 -1.090177 -0.457338 0.575371 -0.754541 4.728107 3.863683 -0.211686 -0.920197 -0.567453

2139 rows × 24 columns

plt.figure(figsize=(12, 10))
sns.heatmap(X_scaled, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Heatmap of X_scaled and Target Variable')
plt.show()

png

Logistic Regression:

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

logreg_model = LogisticRegression(max_iter=10000)
logreg_model.fit(X_train_scaled, y_train)
/Users/brashonford/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:1184: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
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LogisticRegression(max_iter=10000)
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LogisticRegression
LogisticRegression(max_iter=10000)
kcluster = KMeans(n_clusters=10, n_init=10, max_iter=100, random_state=6)

kcluster.fit(X_scaled)
kcluster = KMeans(n_clusters=10, n_init=10, max_iter=100, random_state=6)

kcluster.fit(X_scaled)
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KMeans(max_iter=100, n_clusters=10, n_init=10, random_state=6)
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KMeans
KMeans(max_iter=100, n_clusters=10, n_init=10, random_state=6)
X_scaled
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
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}
</style>
time trt age wtkg hemo homo drugs karnof oprior z30 ... str2 strat symptom treat offtrt cd40 cd420 cd80 cd820 cid
0 0.235799 0.424960 1.464542 1.107649 -0.303123 -1.396547 -0.388893 0.771836 -0.149888 -1.106116 ... -1.189210 -1.090177 -0.457338 0.575371 -0.754541 0.603131 0.730927 -0.876151 -1.374260 -0.567453
1 0.420600 1.311779 2.957595 -1.936862 -0.303123 -1.396547 -0.388893 -0.923192 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 -0.754541 -1.590108 -1.060207 -1.238586 -0.834779 1.762262
2 0.280288 1.311779 1.119991 1.005025 -0.303123 0.716052 2.571400 -0.923192 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 1.325309 -0.206680 -0.672935 2.242044 2.152597 -0.567453
3 0.981848 1.311779 1.349692 0.765569 -0.303123 0.716052 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 -0.754541 -0.535666 0.156934 1.256802 0.068852 -0.567453
4 0.721757 -1.348678 0.890291 -0.636959 -0.303123 0.716052 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 -1.738009 -0.754541 1.294845 -0.126605 -0.242930 -0.344750 -0.567453
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2134 0.725180 1.311779 -1.636415 -1.646094 3.298990 -1.396547 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 1.325309 -1.674463 -1.814005 -0.886566 -0.484116 -0.567453
2135 -1.656702 -1.348678 -2.095816 2.099680 3.298990 -1.396547 -0.388893 0.771836 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 -1.738009 1.325309 0.189790 -1.060207 1.608823 0.212713 -0.567453
2136 0.769669 0.424960 2.038793 -0.397503 3.298990 0.716052 -0.388893 -0.923192 -0.149888 0.904064 ... 0.840894 1.134907 -0.457338 0.575371 -0.754541 0.577824 -0.050533 0.842293 0.237440 -0.567453
2137 -1.417145 -1.348678 -2.440366 -1.140667 3.298990 -1.396547 -0.388893 0.771836 -0.149888 -1.106116 ... -1.189210 -1.090177 -0.457338 -1.738009 -0.754541 -1.556366 -1.399071 0.025772 2.028966 1.762262
2138 0.567756 1.311779 1.119991 0.164003 3.298990 -1.396547 -0.388893 0.771836 -0.149888 -1.106116 ... -1.189210 -1.090177 -0.457338 0.575371 -0.754541 4.728107 3.863683 -0.211686 -0.920197 -0.567453

2139 rows × 24 columns

k = 10
kmeans = KMeans(n_clusters=k, n_init=n_init_value, random_state=42)
clusters = kmeans.fit_predict(X_scaled)
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
X_pca
array([[-2.36230143,  0.31678765],
       [ 2.85345536, -0.68877654],
       [ 1.74057045,  1.33389314],
       ...,
       [ 1.83036617,  1.88692201],
       [-0.83757543, -2.92036584],
       [-3.29896557,  3.58227713]])

plt.scatter(X_pca[:, 0], X_pca[:, 1], c=clusters, cmap='viridis')
plt.title('Cluster Visualization')
plt.show()

png

kcluster.labels_
array([1, 6, 3, ..., 8, 8, 9], dtype=int32)

print(X_scaled_clustered.columns)
Index(['time', 'trt', 'age', 'wtkg', 'hemo', 'homo', 'drugs', 'karnof',
       'oprior', 'z30', 'zprior', 'preanti', 'race', 'gender', 'str2', 'strat',
       'symptom', 'treat', 'offtrt', 'cd40', 'cd420', 'cd80', 'cd820',
       'klabel'],
      dtype='object')

X_scaled_clustered = pd.DataFrame(X_scaled, columns=X.columns.copy())

X_scaled_clustered['klabel'] = kcluster.labels_

X_clustered = X.copy()

X_clustered['klabel'] = kcluster.labels_

X_scaled_clustered = X_scaled.copy()
print(X_clustered.head())
   time  trt  age     wtkg  hemo  homo  drugs  karnof  oprior  z30  ...  str2  \
0   948    2   48  89.8128     0     0      0     100       0    0  ...     0   
1  1002    3   61  49.4424     0     0      0      90       0    1  ...     1   
2   961    3   45  88.4520     0     1      1      90       0    1  ...     1   
3  1166    3   47  85.2768     0     1      0     100       0    1  ...     1   
4  1090    0   43  66.6792     0     1      0     100       0    1  ...     1   

   strat  symptom  treat  offtrt  cd40  cd420  cd80  cd820  klabel  
0      1        0      1       0   422    477   566    324       1  
1      3        0      1       0   162    218   392    564       6  
2      3        0      1       1   326    274  2063   1893       3  
3      3        0      1       0   287    394  1590    966       7  
4      3        0      0       0   504    353   870    782       2  

[5 rows x 24 columns]

import matplotlib.pyplot as plt
for i in range(10):
    plt.plot(kcluster.cluster_centers_[i], label=f'Cluster {i}')
    plt.xticks(range(24), X_clustered, rotation=45)
plt.legend()
<matplotlib.legend.Legend at 0x2a7813190>

png

def plot_cluster_and_centroid(label):
    cluster_data = X_clustered[X_clustered.klabel == label][X_clustered.columns].T
    cluster_data.plot(legend=True)
    plt.plot(kcluster.cluster_centers_[label], 'ko--')
    plt.xticks(range(24), X_clustered.columns, rotation=45)
    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)

plot_cluster_and_centroid(2)

png

grouped_df =X_clustered.groupby(['klabel' , 'cd420']).size().reset_index(name='count')
print(grouped_df.head())
   klabel  cd420  count
0       0     49      1
1       0     50      1
2       0     52      1
3       0     74      1
4       0     81      2

sns.clustermap(X_scaled, method = 'average', metric = 'euclidean', figsize = (15,40))
<seaborn.matrix.ClusterGrid at 0x17a23e610>

png

ANOVA & MANOVA

import numpy as np
import pandas as pd
from statsmodels.stats.anova import AnovaRM
import pandas as pd
from statsmodels.multivariate.manova import MANOVA
y_df = pd.DataFrame(y, columns=['cid'])


combined_data = pd.concat([X, y_df], axis=1)


print(combined_data.head())
   time  trt  age     wtkg  hemo  homo  drugs  karnof  oprior  z30  ...  str2  \
0   948    2   48  89.8128     0     0      0     100       0    0  ...     0   
1  1002    3   61  49.4424     0     0      0      90       0    1  ...     1   
2   961    3   45  88.4520     0     1      1      90       0    1  ...     1   
3  1166    3   47  85.2768     0     1      0     100       0    1  ...     1   
4  1090    0   43  66.6792     0     1      0     100       0    1  ...     1   

   strat  symptom  treat  offtrt  cd40  cd420  cd80  cd820  cid  
0      1        0      1       0   422    477   566    324    0  
1      3        0      1       0   162    218   392    564    1  
2      3        0      1       1   326    274  2063   1893    0  
3      3        0      1       0   287    394  1590    966    0  
4      3        0      0       0   504    353   870    782    0  

[5 rows x 24 columns]

python jupyter={"outputs_hidden": true} X

python jupyter={"outputs_hidden": true} y

formula = 'trt + age + homo + gender ~ cid'
manova = MANOVA.from_formula(formula, data=combined_data)
result = manova.mv_test()

python jupyter={"outputs_hidden": true} print(result)

import statsmodels.api as sm
import statsmodels.formula.api as smf

model = smf.ols('cd40 ~ cid' , data= combined_data).fit()
aov_table = sm.stats.anova_lm(model)
aov_table
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df sum_sq mean_sq F PR(>F)
cid 1.0 1.036000e+06 1.036000e+06 76.279993 4.870459e-18
Residual 2137.0 2.902377e+07 1.358155e+04 NaN NaN