Project Name: Predicting Chronic Kidney Disease (CKD) Using Electronic Health Records (EHR)
Hands-on Project for Data Analytics Students – Beginner to Intermediate
Healthcare is one of the fastest-growing domains for data analytics. Hospitals across India, including Pune, are increasingly using analytics to improve early diagnosis, reduce treatment costs, and enhance patient outcomes.
This project gives students real-world experience in healthcare analytics by predicting Chronic Kidney Disease (CKD) using patient medical records.
Why This Project Is Important for Students?
Healthcare generates massive real-world datasets — perfect for building analytics skills.
Chronic Kidney Disease affects millions in India; early detection can save lives.
Students learn data cleaning, medical dataset handling, model building, evaluation, and ethics — all essential for a data analytics job.
This project is widely asked in interviews for Data Analyst / ML Analyst roles.
Problem Statement
Hospitals collect large amounts of patient data through Electronic Health Records (EHR).
Your task is to analyze these records, clean and preprocess the data, and build a predictive model to identify patients who may have Chronic Kidney Disease.
The goal is to assist healthcare professionals with early detection, helping them plan timely interventions.
Step-by-Step Project Workflow
1. Load and Inspect the Dataset
Students begin by loading the dataset using pandas, checking.
2. Data Cleaning & Preprocessing
Healthcare datasets are often messy.
3. Exploratory Data Analysis (EDA)
Students perform visual exploration using matplotlib and seaborn to understand correlation.
4. Feature Selection & Model Building
Build classification models using:
✔ Logistic Regression
✔ Random Forest Classifier
✔ XGBoost
5. Model Evaluation
6. Data Ethics & Bias Detection
7. Final Output
A trained machine learning model that predicts the likelihood of Chronic Disease based on patient attributes.
you will generate:
Final report
Jupyter notebook with code
Visuals & insights
Model performance summary
Data Source:
UCI Machine Learning Repository — CKD Dataset
Tools/Libraries: Pandas, NumPy, scikit-learn, matplotlib, seaborn
Outcome:
A trained model that predicts the likelihood of Chronic Kidney Disease based on patient attributes.
Description:
Students analyze real patient data containing metrics like blood pressure, glucose level, and serum creatinine. The project includes cleaning messy medical data, handling missing values, and exploring correlations between biomarkers and disease outcomes. Students build classification models (Logistic Regression, Random Forest, XGBoost) and evaluate them with accuracy/ROC-AUC. The project highlights the importance of data ethics and bias detection. It also teaches practical healthcare analytics skills used in hospitals.
What Students Learn from This Project
✔ Real-world medical dataset handling
✔ Data cleaning, transformation & preprocessing
✔ Exploratory Data Analysis (EDA)
✔ Building ML classification models
✔ Model evaluation with ROC-AUC
✔ Understanding biomarker patterns
✔ Ethical issues in healthcare analytics
✔ Domain knowledge in EHR datasets
✔ Creating end-to-end data analytics project
This project simulates actual work done by hospital analytics teams, diagnostic labs, and AI healthcare startups.
Looking to do projects in Advanced Data Analytics:
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