ML_DiabetesPrediction

Prediksi diabetes menggunakan Machine Learning

Diabetes prediction using machine learning

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

I. Requirements:

• Anaconda/Jupyter Notebook
• Python 3.8
• Scikit-learn
• Pandas
• Matplotlib
• Numpy

II. Methods:

• Data Preparation: Load data, review data, inspect, clean data, molding
• Choose Algorithm: Naive Bayes
• Model training: Data splitting, Preprocessing, Feature selection, model training
• Accuracy testing: Evaluate the model, performance enhancing (Random Forest Classifier, Logistic Regression)
• Cross Validation

III. Results:

3.1. Naive bayes:

Accuracy test (naive bayes): 0.7359

Confusion Matrix

[[118 33]
[ 28 52]]

Classification Report precision recall f1-score support

       0       0.81      0.78      0.79       151
       1       0.61      0.65      0.63        80

accuracy                           0.74       231

macro avg 0.71 0.72 0.71 231 weighted avg 0.74 0.74 0.74 231

3.2. Random Forest Classifier

Accuracy test (random forest): 0.7100

Confusion Matrix

[[121 30]
[ 37 43]]

Classification Report precision recall f1-score support

       0       0.77      0.80      0.78       151
       1       0.59      0.54      0.56        80

accuracy                           0.71       231

macro avg 0.68 0.67 0.67 231 weighted avg 0.70 0.71 0.71 231

3.3. Logistic Regression:

Accuracy test (logistic regression): 0.7143

Confusion Matrix:

[[105 46]
[ 20 60]]

Classification Report precision recall f1-score support

       0       0.84      0.70      0.76       151
       1       0.57      0.75      0.65        80

accuracy                           0.71       231

macro avg 0.70 0.72 0.70 231 weighted avg 0.75 0.71 0.72 231

3.4. Logistic Regression CV:

Accuracy test (logistic regression CV): 0.7013

Confusion Matrix:

[[109 42]
[ 27 53]]

Classification Report precision recall f1-score support

       0       0.80      0.72      0.76       151
       1       0.56      0.66      0.61        80

accuracy                           0.70       231

macro avg 0.68 0.69 0.68 231 weighted avg 0.72 0.70 0.71 231