DEV Community: AbuBakar Shabbir

🩺 NephroPredict: Machine Learning for Chronic Kidney Disease Detection

AbuBakar Shabbir — Sun, 07 Sep 2025 20:31:58 +0000

Chronic Kidney Disease (CKD) is a global health concern affecting millions of people worldwide. Early detection is crucial, as timely intervention can significantly reduce the need for dialysis and kidney transplants.

NephroPredict is a machine learning-based project designed to provide an efficient solution for early CKD detection using clinical and laboratory data.

Project Overview

Goal: Early detection of CKD using predictive machine learning models
Dataset: Chronic Kidney Disease dataset (UCI Repository)
Approach: Implemented multiple ML models such as Logistic Regression, KNN, SVM, Decision Tree, and Random Forest
Tuning: Applied hyperparameter tuning (GridSearchCV) to improve model performance
Evaluation Metrics: Accuracy, Precision, Recall, F1-score, and Cross-validation scores

Attributes Used

The dataset consists of important medical attributes, including:

Blood Pressure (Bp), Specific Gravity (Sg), Albumin (Al), Sugar (Su), Red Blood Cells (Rbc), Blood Urea (Bu), Serum Creatinine (Sc), Sodium (Sod), Potassium (Pot), Hemoglobin (Hemo), White Blood Cell Count (Wbcc), Red Blood Cell Count (Rbcc), Hypertension (Htn), Class (Target Variable)

Models & Results

The following models were implemented and compared before and after hyperparameter tuning:

Logistic Regression
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Decision Tree
Random Forest

Key Findings

Logistic Regression and KNN showed slight improvement after tuning, but also signs of overfitting
SVM significantly improved after tuning, highlighting the importance of kernel and regularization
Decision Tree accuracy dropped after tuning, showing sensitivity to depth and splitting criteria
Random Forest consistently achieved the best results with 100% training accuracy and 96.55% test accuracy, making it the most robust model

Conclusion: Random Forest is the most reliable model for this dataset and is recommended for CKD prediction.

📈 Visualization

The project includes professional visualizations such as styled tables and bar charts for comparing model performance.

Repository

The complete source code, dataset, and notebook are available on GitHub:

🔗 Machine Learning for Chronic Kidney Disease Detection

👨‍💻 Author

Developed by Abubakar Shabbir

📜 License

This project is licensed under the MIT License – feel free to use, modify, and share.

✨ If you found this project helpful, don’t forget to leave a ⭐ on GitHub!

# CardioInsight: Machine Learning-Based Heart Risk Prediction

AbuBakar Shabbir — Fri, 05 Sep 2025 13:56:32 +0000

CardioInsight: Machine Learning-Based Heart Risk Prediction

Project Overview

CardioInsight is an advanced machine learning project designed to predict cardiovascular risk in patients using clinical data. By analyzing key features such as age, cholesterol levels, and chest pain type, the system identifies high-risk individuals with remarkable accuracy, supporting early detection and preventive care. The project uses a Random Forest Classifier as the primary model along with feature selection techniques to ensure reliable and interpretable predictions.

Multiple models were trained and evaluated, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, and Random Forest, to select the best-performing model for deployment. Detailed evaluation metrics were recorded to compare performance and reliability.

Key Features

High-Performance Prediction: Achieves over 98% test accuracy with Random Forest.
Insightful Feature Analysis: Identifies the most significant clinical features affecting heart disease risk.
Data Visualization: Correlation heatmaps, feature importance plots, and model evaluation charts included.
Scalable & Modular: Easily adaptable to new datasets or alternative ML models.
Multi-Model Evaluation: Allows comparison of multiple models to select the most effective one.

Trained Models & Metrics

Model	Accuracy	Precision	Recall	F1-Score
Logistic Regression	86.6412	83.8509	93.75	88.5246
Decision Tree	97.3282	97.2414	97.9167	97.5779
Random Forest	98.4733	97.2973	100	98.6301
Gradient Boosting	96.9466	95.9459	98.6111	97.2603
SVM	89.6947	87.7419	94.4444	90.9699
KNN	87.0229	89.2857	86.8056	88.0282

Top Features

The following features were found to be most important in predicting heart disease risk:

Age
Cholesterol levels
Chest pain type (Typical angina)

How to Use

Clone the repository:

git clone https://github.com/Abubakar-Shabbir/HeartScope-Predictive-ML-for-Cardiovascular-Risk.git

Install the dependencies:

pip install -r requirements.txt

Open the Jupyter Notebook Navigate to the notebook file to explore data analysis, modeling, and visualizations: Notebooks/HeartScope_Predictive ML for Cardiovascular Risk.ipynb

Author

Abubakar Shabbir

License

GitHub Repository: CardioInsight: Machine Learning-Based Heart Risk Prediction

Election Management System (EMS) – Secure Web-Based Digital Voting Platform

AbuBakar Shabbir — Wed, 20 Aug 2025 15:51:25 +0000

I, Abubakar Shabbir, built the Election Management System (EMS), a modern, secure, and user-friendly digital voting web application using Python, Django, MySQL, and Bootstrap. This platform provides a transparent and efficient way to manage elections, handle voter registration, and monitor results in real-time.

🔹 Key Features

Secure Voter Management: Register voters and allow secure logins with OTP verification.
OTP Authentication: Email OTP ensures only verified voters can access the system.
Role-Based Access: Separate dashboards for Admins, Voters, and Candidates.
Admin Dashboard: Add and manage candidates, control elections, monitor voters, and view real-time results.
Voting Panel: One vote per voter linked to a unique CNIC, preventing duplicate voting.
Real-Time Election Results: Display results by constituency and party for transparency.
Deployment Ready: Can run locally or on a live server with MySQL backend.

🔹 Technology Stack

Backend: Python, Django
Frontend: Bootstrap, HTML, CSS
Database: MySQL
Authentication: OTP via Gmail SMTP

🔹 Why EMS is Valuable

This project is ideal for secure election management for educational institutions, organizations, or local communities. It emphasizes security, transparency, and user experience, making voting easier and tamper-proof.

🔹 Voter Panel

The Voter Panel displays only the elections that have been created and approved by the admin. Each voter can view the elections they are eligible for and cast their vote securely within the specified election. This ensures role-specific access and prevents any unauthorized voting.

🔹 About Me

This project was developed by Abubakar Shabbir, focusing on secure web applications and modern software engineering practices.

GitHub Repository: Election Management System

Relative Grade Calculator

AbuBakar Shabbir — Tue, 17 Sep 2024 14:24:45 +0000

Check out this is a relative grade calculator. By entering your own marks and highest marks of class you can find your grade according to university grading process