{"repo":"Paramesh-Mandapaka/Heart-Disease-Analysis-Power-BI-Project","free":true,"listed":false,"github":"https://github.com/Paramesh-Mandapaka/Heart-Disease-Analysis-Power-BI-Project","clone":"git clone https://github.com/Paramesh-Mandapaka/Heart-Disease-Analysis-Power-BI-Project.git","description":"Heart Disease Analysis Power BI Dashboard A data-driven Power BI report analyzing heart disease patient data to uncover insights by gender, age, and health metrics. Built using Power BI, Excel, and DAX to demonstrate data modeling, visualization, and business intelligence storytelling for healthcare analytics.","language":null,"stars":13,"topics":["bi-project","business-intelligence","data-analysis","data-analyst-portfolio","data-analytics","data-modeling","data-visualization","dax","healthcare-analytics","hospital-analytics"],"license":null,"category":"dashboards-admin","readme_excerpt":"Heart-Disease-Analysis-Power-BI-Project --- Project Overview This Power BI Report provides an end-to-end analysis of Heart Disease Patients to help hospital management identify critical health patterns and improve patient outcomes. The dashboard visualizes patient distribution by gender , age , and health metrics , providing valuable insights into heart disease risk factors and overall trends. --- Business Objectives - Analyze the demographics of heart disease patients by gender and age. - Identify patterns in cholesterol levels, blood pressure, and heart rate . - Evaluate survival outcomes based on key medical indicators. - Support hospital decision-making for better treatment planning. - Enhance data-driven health management through actionable visuals. --- Key Insights Male Patients - Higher occurrence rate observed in middle-aged male patients (40–60 years). - Elevated cholesterol and resting BP are major contributing factors. - Lower survival rate in patients with low ejection fraction . Female Patients - Females show fewer heart disease cases , but higher survival probability. - Key risk indicators include serum creatinine and age . - Lifestyle-based prevention can significantly reduce hospitalization rates. --- Technical Details Aspect Details ------------- ------------- Tools Used Power BI, Microsoft Excel Visual Types Line Chart, Ribbon Chart, Clustered Column Chart, KPI Cards, Donut Chart Data Source UCI Heart Failure Clinical Records Dataset Purpose To analyze heart","default_branch":null,"files":null,"tree":[],"storefront":"/r/Paramesh-Mandapaka","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Paramesh-Mandapaka/Heart-Disease-Analysis-Power-BI-Project/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}