Heart_Disease_Diagnostic_Analysis

Objective 🎯

The aim of this project is to use the given data and perform ETL and data analysis to infer key metrics and patterns in the dataset. In addition to this, different visualizations are developed to depict meaningful relationships.

Problem Statement ❓

◾ Health is real wealth in the pandemic time we all realized the brute effects of covid-19 on all irrespective of any status. You are required to analyze this health and medical data for better future preparation.

◾ As it is rightly said, ‘Health is Wealth’. We have realized this fact in the pandemic time after witnessing the brute effects of Covid-19 on people of all age groups. Apart from this, another major contributor to the death rate is heart-related diseases

◾ Heart diseases have been known to take a major toll on people’s lives. As a layman, we may feel that the common factors for heart-related diseases are cardiac arrest or blockages. But the dataset under analysis describes multiple different medical parameters associated with the heart and their typical values. We will be analyzing the relationships between them and studying the implications of changes in those parameters. In this project, we will be incorporating the most trending and powerful BI tool namely Tableau.

Tools 🛠

1.Jupyter Notebook

2.Pandas

3.NumPy

4.Matplotlib

5.MS Excel

6.Tableau

Approach For Data Analysis

◾ Data Extraction

◾ Data Preprocessing

◾ Data Exporting

◾ Dataset Loading and Modification

◾ Data Analysis

◾ Deployment

KEY PERFORMANCE INDICATOR (KPI)

Key indicators displaying a summary of the heart disease and its relationship with different metrics

1. Percentage of People Having Heart Disease

2. Variation of ‘thal’ (Thalassemia type) with ‘sex’

3. Variation of ‘chol’ (Cholesterol), ‘trestbps’ (Resting blood pressure) with ‘fbs’ (Fasting Blood Sugar).

4. Variation of ‘exang’ (Exercise induced angina) with ‘cp’ (Chest Pain type).

5. Variation of ‘num’ (Angiographic disease status) with ‘sex’.

6. Variation of the ‘age’ with ‘chol’ (Cholesterol) and ‘sex’

7. Variation of ‘cp’ (Chest Pain type) with ‘sex’

8. Variation of ‘thalach’ (Maximum heart rate) with ‘age’

9. Variation of ‘restecg’ (Resting electrocardiograph results) with ‘sex’

10. Variation of ‘slope’ (Slope of the peak exercise ST segment), ‘restecg’ (Resting Electrocardiograph results) and ‘oldpeak’ (ST depression induced by exercise relative to rest)

Conclusion 💡

◾ 45.87% of People suffer from heart disease.

◾ Elderly Aged Men are more (50 to 60 Years) and Females are more in 55 to 65 Years Category

◾ Males are more prone to heart disease.

◾ Elderly Aged People are more prone to heart disease.

◾ People having asymptomatic chest pain have a higher chance of heart disease.

◾ High cholesterol levels in people having heart disease.

◾ Blood Pressure increases between the age of 50 to 60 and somehow continues till 70.

◾ Cholesterol and maximum heart rate Increased in the age group of 50-60.

◾ ST depression mostly increases between the age group of 30-40

Insights 📊

Youtube Video 🎬

https://youtu.be/lbfsON1k-sY