minha12 / detectingdiabetedisease

Preliminary

XGBoost will be used for this analysis.

What is XGBoost?

XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree based algorithms are considered best-in-class right now.

!pip install xgboost

Collecting xgboost
  Downloading xgboost-1.2.1-py3-none-macosx_10_13_x86_64.macosx_10_14_x86_64.macosx_10_15_x86_64.whl (1.2 MB)
�[K     |████████████████████████████████| 1.2 MB 3.9 MB/s eta 0:00:01
�[?25hRequirement already satisfied: scipy in /opt/miniconda3/lib/python3.7/site-packages (from xgboost) (1.4.1)
Requirement already satisfied: numpy in /opt/miniconda3/lib/python3.7/site-packages (from xgboost) (1.18.1)
Installing collected packages: xgboost
Successfully installed xgboost-1.2.1

Dataset

Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Content

The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Source:

https://www.kaggle.com/uciml/pima-indians-diabetes-database

import pandas as pd

dataset = pd.read_csv('diabetes.csv')

dataset.head()

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	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

Pre-processing dataset

• Split train/set dataset

from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

features = dataset.iloc[:, 0:8].values
labels = dataset.iloc[:,8].values

features

array([[  6.   , 148.   ,  72.   , ...,  33.6  ,   0.627,  50.   ],
       [  1.   ,  85.   ,  66.   , ...,  26.6  ,   0.351,  31.   ],
       [  8.   , 183.   ,  64.   , ...,  23.3  ,   0.672,  32.   ],
       ...,
       [  5.   , 121.   ,  72.   , ...,  26.2  ,   0.245,  30.   ],
       [  1.   , 126.   ,  60.   , ...,  30.1  ,   0.349,  47.   ],
       [  1.   ,  93.   ,  70.   , ...,  30.4  ,   0.315,  23.   ]])

labels[0:5]

array([1, 0, 1, 0, 1])

Visualizing Outcome

Plotting percentage of Outcome 0 (no disease) vs 1 (disease)

dataset.Outcome.value_counts().plot(kind='pie')

<matplotlib.axes._subplots.AxesSubplot at 0x1a26c3ed50>

print(f'Percentage of No Disease: {100 * labels[labels==0].shape[0] / labels.shape[0]:0.2f}')
print(f'Percentage of Disease: {100 * labels[labels==1].shape[0] / labels.shape[0]:0.2f}')

Percentage of No Disease: 65.10
Percentage of Disease: 34.90

Normalize data

from sklearn.preprocessing import MinMaxScaler

# Scalling to range (-1, 1)
scaler=MinMaxScaler( (-1, 1) )
X = scaler.fit_transform(features)
#X = features
Y = labels

X

array([[-0.29411765,  0.48743719,  0.18032787, ...,  0.00149031,
        -0.53116994, -0.03333333],
       [-0.88235294, -0.14572864,  0.08196721, ..., -0.2071535 ,
        -0.76686593, -0.66666667],
       [-0.05882353,  0.83919598,  0.04918033, ..., -0.30551416,
        -0.49274125, -0.63333333],
       ...,
       [-0.41176471,  0.2160804 ,  0.18032787, ..., -0.21907601,
        -0.85738685, -0.7       ],
       [-0.88235294,  0.26633166, -0.01639344, ..., -0.10283159,
        -0.76857387, -0.13333333],
       [-0.88235294, -0.06532663,  0.14754098, ..., -0.09388972,
        -0.79760888, -0.93333333]])

Splitting train/test sets

x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size = 0.33, random_state=0)

x_train.shape

(514, 8)

x_test.shape

(254, 8)

Build and fit model

model = XGBClassifier()
model.fit(x_train, y_train)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=0.300000012, max_delta_step=0, max_depth=6,
              min_child_weight=1, missing=nan, monotone_constraints='()',
              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
              tree_method='exact', validate_parameters=1, verbosity=None)

Prediction and evaluation

y_pred = model.predict(x_test)
y_pred

array([1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,
       1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0,
       1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
       0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0,
       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
       0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

# accuracy
acc = accuracy_score(y_test, y_pred)
print(f'Accuracy score: {acc * 100:0.2f}')

Accuracy score: 75.98

Summary

What we have learned:

• Quick load and visualize dataset
• Using MinMaxScaler
• Using XGBoost