Diabetes classification

The dataset contains 8 medical conditions features (X) :

Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age

Outcome is present a binary target (y) label

0 : Diabetes False

1 : Diabetes True

dimension of diabetes data: (768, 9)

• Dataset is small but well labeled. There are no null values present.
• very suitable to supervised machine learning formulation.
• This is a binary classification problem, where we have 2 classes in the target (y) (i.e.df['Outcome']) and the medical conditions can be used as the feature (X).

Machine-Learning Models

I have used 8 different machine learning classifiers to diabetes classfication :

• K-Nearest Neighbors (kNN)
• Logistic regression
• Decision Tree
• Random Forest
• Gradient Boosting
• Support Vector Machine (SVM)
• Neural Networks (Multi-level Perceptron : MLP)
• XGBoost

Results are Shown below

Confusion Matricies

Conclusion

• Logistic regression and Neural Netowrks seems to provide the best performance based on 10-fold cross validation of the dataset. Logistic regression achieves a higher F1-score as well, which is better metric for model evalution.
• From the confusion matricies, decision tree has the highest success in detecting the diabetes.
• Feature selection suggests the Glucose is the most crucial factor for the successful prediction of diabetes.

Dashboard