itushar-bhatt/Heart_disease-analysis
A data visualization project using Power BI to explore and analyze heart disease patterns based on age, gender, cholesterol, chest pain type, and more.
❤️ Heart Disease Prediction & Analysis - Power BI Dashboard
📊 Overview
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.
Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.
People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.
This project involves a detailed Power BI analysis of heart disease prediction data. Using a dataset sourced from Kaggle, the aim was to derive actionable insights into the factors contributing to heart disease, segmented by gender, age, cholesterol, chest pain types, and other medical indicators.
🔍 Dataset Source
- Name: Heart Failure Prediction Dataset
- Source: Kaggle - Heart Failure Prediction
- Records: 918
- Attributes: 12 medical features + target label
📁 Files
heart_disease_analysis.pbix: The main Power BI project file.heart.csv: Dataset file (from Kaggle).
🧠 Project Objectives
- Predict and analyze heart disease cases based on various health metrics.
- Visualize key health indicators like age, cholesterol, max heart rate, and ST depression.
- Segment analysis across gender, chest pain types, and smoking/alcohol habits.
- Display survival rate and risk distribution in interactive visuals.
📌 Key Attributes in Dataset
| Feature | Description |
|---|---|
| Age | Patient age |
| Sex | Gender (1 = Male, 0 = Female) |
| ChestPainType | Type of chest pain |
| RestingBP | Resting blood pressure (mm Hg) |
| Cholesterol | Serum cholesterol (mg/dl) |
| FastingBS | Fasting blood sugar (>120 mg/dl) |
| RestingECG | Resting electrocardiogram results |
| MaxHR | Maximum heart rate achieved |
| ExerciseAngina | Exercise-induced angina (Yes/No) |
| Oldpeak | ST depression induced by exercise |
| ST_Slope | Slope of peak exercise ST segment |
| HeartDisease | Target variable (1 = Yes, 0 = No) |
📌 Power BI Visuals & Features
1️⃣ First Slide - Hero Section
- Eye-catching header image
- Text introduction with project title
- Source attribution
2️⃣ Summary Cards
- 💓 % of patients with heart disease
- 👨 Male vs 👩 Female Heart Disease cases
- 📉 Average Cholesterol & Max Heart Rate
- 🧠 ST Depression average
3️⃣ Charts & Visualizations
- Bar Chart: Heart Disease by Gender
- Stacked Column: Heart Disease by Chest Pain Type
- Scatter Plot: Cholesterol vs Max Heart Rate (colored by Heart Disease)
- Age Group Survival Rate (Grouped column)
- Line Chart: ST depression by Age
4️⃣ Filters & Slicers
- Gender
- Chest Pain Type
- Smoking/Alcohol Consumption
- Age Groups (Custom-created in Power Query)
5️⃣ Interactive Features
- Image Button to trigger filter pane.
- Home, gender, overview and filter buttons.
- Index column added in Power Query for tracking.
🔧 Power Query Transformations
- Renamed columns for clarity
- Added Index Column
- Created custom Age Group buckets:
- 20–29, 30–39, ..., 70–79, 80+
- Converted binary columns into readable format (e.g., 1 → "Yes", 0 → "No")
🧠 Key Insights
- 🚹 Males are more prone to heart disease in this dataset.
- 🩺 Patients with asymptomatic chest pain have a higher risk.
- 📈 High cholesterol doesn’t always correlate with max heart rate.
- 📊 The survival rate decreases with age.
- 🚬 Smoking and alcohol usage patterns are more common in heart disease patients.
🏁 Conclusion
This Power BI report provides a comprehensive, interactive, and visual-first approach to understanding and predicting heart disease factors. The dashboard is ideal for both medical researchers and data analysts aiming to explore healthcare data analytics.
🧾 Credits
- Dataset by Fedesoriano on Kaggle.
- Visualizations by Tushar Bhatt
- Designed and developed in Microsoft Power BI Desktop
🖼 Preview
You can check the preview screenshot on preview folder.