EDA : Lower Back Pain

 Exploratory Data Analysis on Lower Back Pain

Lower Back Pain

Lower back pain, also called lumbago, is not a disorder. It’s a symptom of several different types of medical problems. It usually results from a problem with one or more parts of the lower back, such as:

• ligaments
• muscles
• nerves
• the bony structures that make up the spine, called vertebral bodies or vertebrae

It can also be due to a problem with nearby organs, such as the kidneys.

According to the American Association of Neurological Surgeons, 75 to 85 percent of Americans will experience back pain in their lifetime. Of those, 50 percent will have more than one episode within a year. In 90 percent of all cases, the pain gets better without surgery. Talk to your doctor if you’re experiencing back pain.

In this Exploratory Data Analysis (EDA) I am going to use the Lower Back Pain Symptoms Dataset and try to find out ineresting insights of this dataset.

#pip install xgboost

if xgboost is throws errors

ModuleNotFoundError Traceback (most recent call last)

import os

os.getcwd()

os.chdir('C:\\Users\\kt.rinith\\Google Drive\\Training\\PGP-DSBA\\Jupiter Files')

# change working directory

dataset = pd.read_csv("backpain.csv")

dataset.head() # this will return top 5 rows 

# This command will remove the last column from our dataset.

#del dataset["Unnamed: 13"]

dataset.describe()

dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 310 entries, 0 to 309
Data columns (total 13 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   pelvic_incidence          310 non-null    float64
 1   pelvic tilt               310 non-null    float64
 2   lumbar_lordosis_angle     310 non-null    float64
 3   sacral_slope              310 non-null    float64
 4   pelvic_radius             310 non-null    float64
 5   degree_spondylolisthesis  310 non-null    float64
 6   pelvic_slope              310 non-null    float64
 7   Direct_tilt               310 non-null    float64
 8   thoracic_slope            310 non-null    float64
 9   cervical_tilt             310 non-null    float64
 10  sacrum_angle              310 non-null    float64
 11  scoliosis_slope           310 non-null    float64
 12  Status                    310 non-null    object 
dtypes: float64(12), object(1)
memory usage: 31.6+ KB

dataset["Status"].value_counts().sort_index().plot.bar()

dataset.corr()

plt.subplots(figsize=(12,8))
sns.heatmap(dataset.corr())

sns.pairplot(dataset, hue="Status")

Visualize Features with Histogram: A Histogram is the most commonly used graph to show frequency distributions.

dataset.hist(figsize=(15,12),bins = 20, color="#007959AA")
plt.title("Features Distribution")
plt.show()

Detecting and Removing Outliers
plt.subplots(figsize=(15,6))
dataset.boxplot(patch_artist=True, sym="k.")
plt.xticks(rotation=90)

(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
 [Text(1, 0, 'pelvic_incidence'),
  Text(2, 0, 'pelvic tilt'),
  Text(3, 0, 'lumbar_lordosis_angle'),
  Text(4, 0, 'sacral_slope'),
  Text(5, 0, 'pelvic_radius'),
  Text(6, 0, 'degree_spondylolisthesis'),
  Text(7, 0, 'pelvic_slope'),
  Text(8, 0, 'Direct_tilt'),
  Text(9, 0, 'thoracic_slope'),
  Text(10, 0, 'cervical_tilt'),
  Text(11, 0, 'sacrum_angle'),
  Text(12, 0, 'scoliosis_slope')])

Remove Outliers:

# we use tukey method to remove outliers.
# whiskers are set at 1.5 times Interquartile Range (IQR)def  remove_outlier(feature):
    first_q = np.percentile(X[feature], 25)
    third_q = np.percentile(X[feature], 75)
    IQR = third_q - first_q
    IQR *= 1.5    minimum = first_q - IQR # the acceptable minimum value
    maximum = third_q + IQR # the acceptable maximum value
    
    mean = X[feature].mean()    """
    # any value beyond the acceptance range are considered
    as outliers.     # we replace the outliers with the mean value of that 
      feature.
    """    X.loc[X[feature] < minimum, feature] = mean 
    X.loc[X[feature] > maximum, feature] = mean
# taking all the columns except the last one
# last column is the labelX = dataset.iloc[:, :-1]for i in range(len(X.columns)): 
        remove_outlier(X.columns[i])
After removing Outliers:






features distribution after removing outliers
Feature Scaling:
Feature scaling though standardization (or Z-score normalization) can be an important preprocessing step for many machine learning algorithms. Our dataset contains features that vary highly in magnitudes, units and range. But since most of the machine learning algorithms use Euclidean distance between two data points in their computations, this will create a problem. To avoid this effect, we need to bring all features to the same level of magnitudes. This can be achieved 
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(X)
scaled_df = pd.DataFrame(data = scaled_data, columns = X.columns)
scaled_df.head()
Label Encoding:
Certain algorithms like XGBoost can only have numerical values as their predictor variables. Hence we need to encode our categorical values. LabelEncoder from sklearn.preprocessing package encodes labels with values between 0 and n_classes-1.
label = dataset["class"]
encoder = LabelEncoder()
label = encoder.fit_transform(label)
Model Training and Evaluation:

X = scaled_df
y = label
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=0)
clf_gnb = GaussianNB()
pred_gnb = clf_gnb.fit(X_train, y_train).predict(X_test)
accuracy_score(pred_gnb, y_test)
# Out []: 0.8085106382978723
clf_svc = SVC(kernel="linear")
pred_svc = clf_svc.fit(X_train, y_train).predict(X_test)
accuracy_score(pred_svc, y_test)
# Out []: 0.7872340425531915
clf_xgb =  XGBClassifier()
pred_xgb = clf_xgb.fit(X_train, y_train).predict(X_test)
accuracy_score(pred_xgb, y_test)
# Out []: 0.8297872340425532

Feature Importance:

fig, ax = plt.subplots(figsize=(12, 6))
plot_importance(clf_xgb, ax=ax)




















Marginal plot
A marginal plot allows us to study the relationship between 2 numeric variables. The central chart displays their correlation.
Lets visualize the relationship between degree_spondylolisthesis and class:
sns.set(style="white", color_codes=True)
sns.jointplot(x=X["degree_spondylolisthesis"], y=label, kind='kde', color="skyblue")