Diabetes Prediction using Logistic Regression

Shravani Kondibhau Wayal

Completed work

Data Analyst

Data Scientist

ML Engineer

Jupyter

Python

scikit-learn

Healthcare

Diabetes Prediction using Logistic Regression

A machine learning-based classification project to predict whether a person is diabetic or not, based on medical diagnostic data. This project aims to support early diagnosis using logistic regression and fundamental data analysis techniques.

Problem Statement

Diabetes is a growing health concern worldwide. Early detection is crucial to prevent complications such as heart disease, kidney failure, and vision loss. This project builds a machine learning model that can assist healthcare professionals in identifying high-risk individuals by analyzing health metrics such as glucose levels, BMI, and blood pressure.

Potential Use Case

This model could be deployed by healthcare startups in rural India as a mobile-based screening tool. Health workers could collect patient data on-site and use the model to instantly predict diabetes risk — even in low-resource settings with limited internet or medical infrastructure.

Dataset Overview

Name: Pima Indians Diabetes Database

Source: Kaggle Dataset Link

Description: This dataset includes several medical predictor variables and one target variable (Outcome). All patients are females of at least 21 years of age of Pima Indian heritage.

Records: 768

Features

Feature Description Pregnancies Number of pregnancies Glucose Plasma glucose concentration BloodPressure Diastolic blood pressure (mm Hg) SkinThickness Triceps skin fold thickness (mm) Insulin 2-Hour serum insulin (mu U/ml) BMI Body mass index DiabetesPedigree Diabetes pedigree function (family history) Age Age in years Outcome 0 = Non-diabetic, 1 = Diabetic

Exploratory Data Analysis (EDA) Highlights

-Strong correlation observed between glucose levels, BMI, and diabetic outcome.

-Several columns (like insulin, skin thickness) contained zero values which were treated as missing and imputed using median.

-Diabetic patients tend to have higher BMI, more pregnancies, and higher glucose levels.

Feature Engineering

-Handled missing values (zeroes) by replacing them with column medians.

-Scaled features using StandardScaler to improve model performance.

-No feature was dropped — logistic regression performed best with all predictors.

Model Overview

Algorithm: Logistic Regression

Toolkits: Python, Scikit-learn, Pandas, Matplotlib, Seaborn

Train/Test Split: 80/20

Model Evaluation

Metric Value Accuracy 77.9% Precision 77.4% Recall 62.2% F1 Score 69.0% ROC AUC Score 84.7%

Confusion Matrix

[[82 17] [21 34]]

Classification Report

                precision    recall  f1-score   support

           0       0.80      0.83      0.81        99
           1       0.67      0.62      0.64        55
    accuracy                           0.75       154
   macro avg       0.73      0.72      0.73       154
weighted avg       0.75      0.75      0.75       154

ROC Curve and AUC Score

AUC Score: 0.8229568411386594

Model Interpretation

The model performs well overall but shows slightly lower recall for diabetic cases. In a real-world medical setting, false negatives (undetected diabetic cases) are riskier and need to be minimized. A future version could adjust the classification threshold or apply recall-optimized models.

Future Enhancements

-Test other models like Random Forest, Decision Tree, or XGBoost

-Hyperparameter tuning using GridSearchCV

-Use SHAP or LIME for model explainability

-Build a Streamlit app for interactive predictions

-Integrate real-time form inputs for live testing

Dependencies

Make sure you have the following Python libraries installed:

-pandas

-numpy

-matplotlib

-seaborn

-scikit-learn

-jupyter

Get Started

Open the notebook in Jupyter:

  jupyter notebook diabetes_logistic.ipynb

Notes

-Zero values in columns like BMI, BloodPressure, and Glucose were treated as missing and handled appropriately.

-Evaluation is based on one train/test split; performance may vary with cross-validation or different splits.

-The model can be extended to more complex classifiers like Random Forests or XGBoost.

Project Structure

Diabetes-Prediction-model/
├── data/
│   └── diabetes.csv
├── diabetes_logistic.ipynb
└── README.md

Author

Shravani Wayal

Data Science & ML Enthusiast | Python | Analytics email: wayalshravani04@gmail.com

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Completed work

Posted Aug 16, 2025

A ML project to predict whether a person is diabetic or not. This project aims early diagnosis using logistic regression & data analysis techniques.

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