Comprehensive Analysis of Heart Attack Risk Factors data visualization using Power BI

Objectives of the Report
Heart attacks are a leading cause of death globally, and early risk prediction can significantly reduce
mortality rates. This dataset provides a balanced and diverse set of features to help researchers, data
scientists, and machine learning practitioners explore methods for predicting heart attack risks.
The objectives of this report are as follows:
1. Identify Key Risk Factors:
• Analyze the demographic, lifestyle, and health-related factors contributing to heart attack
risks, including age, gender, BMI, stress levels, smoking, alcohol consumption, physical
activity, and chronic conditions like diabetes and hypertension.
2. Understand Patterns and Trends:
• Examine patterns in heart attack risks across different groups (e.g., demographic segments,
BMI ranges, and stress levels) to identify populations at higher risk.
3. Provide Data-Driven Insights:
• Utilize visualizations to highlight correlations and trends in risk factors, enabling stakeholders
to make informed decisions.
4. Recommend Preventive Measures:
• Develop actionable and tailored recommendations to mitigate heart attack risks by addressing
modifiable factors such as obesity, cholesterol, smoking, and physical inactivity.
5. Promote Public Awareness:
• Encourage awareness campaigns and health interventions to educate individuals about the
importance of lifestyle changes and regular health monitoring.
6. Support Strategic Planning:
• Assist healthcare professionals, policymakers, and organizations in designing targeted
programs and policies to improve heart health outcomes.