🩺 Diabetes Prediction Using Classification Method 🩺

Machine Learning & Data-Science Project ( Final Year Project )

Built with Python 3.x • Anaconda • Jupyter-Lab • Scikit-learn • TensorFlow/Keras

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🎯 Project Overview

Item	Details
Goal	Predict whether a patient has diabetes or not.
Approach	Supervised Classification using Neural Network + Classical ML models.
Dataset	Pima Indians Diabetes Dataset (768 rows × 9 columns).
Tools

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📊 1. Exploratory Data Analysis (EDA)

1.1 Quick Peek 👀

import pandas as pd
df = pd.read_csv('diabetes.csv')
df.head()

Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
6	148	72	35	0	33.6	0.627	50	1
1	85	66	29	0	26.6	0.351	31	0

1.2 Summary Statistics 📈

df.describe().T.style.bar(subset=['mean'], color='#5fba7d')

	count	mean	std	min	25%	50%	75%	max
Glucose	768	120.89	31.97	0	99	117	140.25	199
BMI	768	31.99	7.88	0	27.3	32	36.6	67.1

1.3 Visual Insights 📉

Pair-plot showing correlations among features; red points are diabetic (Outcome=1).

• Strongest predictor: Glucose levels
• Missing values: 0 in Insulin & SkinThickness → impute with median.

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🧹 2. Data Pre-processing

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

X = df.drop('Outcome', axis=1)
y = df['Outcome']

# Impute 0's → median
cols = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']
X[cols] = X[cols].replace(0, X[cols].median())

# Scale
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split 70/30
X_train, X_test, y_train, y_test = train_test_split(
    X_scaled, y, test_size=0.30, random_state=42, stratify=y)

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🧠 3. Neural Network (Keras)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout

model = Sequential([
    Dense(16, activation='relu', input_shape=(X_train.shape[1],)),
    Dropout(0.2),
    Dense(8, activation='relu'),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy',
              metrics=['accuracy'])
history = model.fit(X_train, y_train,
                    validation_split=0.15,
                    epochs=100, batch_size=16, verbose=0)

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🧪 4. Classical ML Models

Model	Accuracy	Precision	Recall	F1-Score
Logistic Regression	0.794	0.75	0.64	0.69
Decision Tree	0.739	0.70	0.60	0.65
SVM (RBF)	0.792	0.76	0.62	0.68

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🏆 5. Model Comparison & Best Pick

Metric	Neural Net	Logistic	Decision Tree	SVM
Accuracy	0.844 👑	0.794	0.739	0.792
Precision	0.81	0.75	0.70	0.76
Recall	0.73	0.64	0.60	0.62
F1-Score	0.77	0.69	0.65	0.68

🥇 Neural Network wins with 84.4 % accuracy.

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💾 6. Save Models for Production

# Keras model
model.save('diabetes_nn.h5')

# Sci-kit models
import joblib
joblib.dump(lr, 'diabetes_lr.pkl')
joblib.dump(dt, 'diabetes_dt.pkl')
joblib.dump(svm, 'diabetes_svm.pkl')

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🚀 7. Quick Usage Demo

# Load & predict
from tensorflow.keras.models import load_model
model = load_model('diabetes_nn.h5')

patient = [[6, 148, 72, 35, 0, 33.6, 0.627, 50]]
patient_scaled = scaler.transform(patient)
pred = model.predict(patient_scaled)[0][0]
print("Risk of diabetes: {:.1%}".format(pred))
# → Risk of diabetes: 91.4%

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📁 Project Tree

📦 Diabetes-Prediction/
 ├─ 📁 data/
 │   └─ diabetes.csv
 ├─ 📁 notebooks/
 │   └─ EDA.ipynb
 ├─ 📁 models/
 │   ├─ diabetes_nn.h5
 │   └─ *.pkl
 ├─ 📁 src/
 │   ├─ train.py
 │   └─ predict.py
 ├─ 📄 requirements.txt
 └─ 📄 README.md

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📚 Requirements (requirements.txt)

pandas==2.2.2
numpy==1.26.4
matplotlib==3.9.0
seaborn==0.13.2
scikit-learn==1.5.0
tensorflow==2.17.0
joblib==1.4.2

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🤝 Contributing

Feel free to open issues or PRs to improve the model or add new features (e.g., SHAP explainability, Streamlit GUI).

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📄 License

MIT © 2025 Diabetes-Prediction-Team

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“Early diagnosis saves lives.”

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👨💻 By: Irfan Ullah Khan