├── disease analysis.jpg ├── report healthcare.jpg ├── healthcare_analysis.pbix ├── patient care analysis.jpg └── README.md /disease analysis.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jey-krishna/Healthcare-Analysis-powerBI/HEAD/disease analysis.jpg -------------------------------------------------------------------------------- /report healthcare.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jey-krishna/Healthcare-Analysis-powerBI/HEAD/report healthcare.jpg -------------------------------------------------------------------------------- /healthcare_analysis.pbix: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jey-krishna/Healthcare-Analysis-powerBI/HEAD/healthcare_analysis.pbix -------------------------------------------------------------------------------- /patient care analysis.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Jey-krishna/Healthcare-Analysis-powerBI/HEAD/patient care analysis.jpg -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Health Care Analysis 2 | Welcome to the Healthcare Analysis project repository! This project aims to analyze a healthcare dataset using Power BI to uncover insights and trends in patient demographics and healthcare costs. 3 | ## Overview 4 | In this project I analyze the healthcare dataset and try to get enough insights based on the data to make better decisions for the patients healthcare. Before that I need to clean, transform, analyze the data, 5 | so thatt it could be more easier to draw enough visualizations. 6 | ## Overview of the dataset 7 | The dataset used in this analysis includes the following columns: 8 | * **Name:** Name of the Patients 9 | * **Age:** Age of the Patiens 10 | * **Gender:** Gender type (male or female) 11 | * **Blood Type:** Blood type of the patients 12 | * **Date of Admision:** Date where the patients admit to the hospital 13 | * **Doctor:** Name of the doctr who diagnosed for the respective patients 14 | * **Hostipal:** Name of the Hospital where the ptitent admitted 15 | * **Insurance Provider:** Name of the insurance company 16 | * **Billing Amount:** Total amont spent by the patients for their treatments 17 | * **Room Number:** The room allocated for the patients 18 | * **Admission Type:** Type of the admission (Urgent, Emergency, Elective) 19 | * **Dischrage Date:** date of the discharge 20 | * **Medication:** Type of the diagnosis 21 | * **Test Results:** Type of the test results (Abnormal, Normal, Inconclusive) 22 | * **Days Spent:** Number of days that the patients spent in the hospital 23 | * **medical condition:** Medical condition of the patients 24 | ## Analysis Steps 25 | 1. **Data Understanding:** Exploring the dataset to understand its structure and contents 26 | 2. **Data Preparation:** Clean and preprocess the data to ensure it's ready for analysis 27 | 3. **Visualization:** Create visualizations that highlight key findings in the data 28 | 4. **Report taking:** Interpret the results of analysis and derive actionable insights that can inform decision-making in the healthcare domain 29 | ## Report of the Analysis 30 | ### Disease Analysis Report: 31 | 32 | 1. Cancer expenses significantly surpass those of other diseases, with arthritis incurring the least expenditure. 33 | 2. A total of 1708 patients suffer from asthma, followed by cancer. 34 | 3. Individuals with AB- blood type are more prone to suffering; notably, those with AB- blood type are predisposed to obesity. 35 | 4. The majority of patients' test results indicate abnormalities 36 | 37 | ### Patient Analysis Report: 38 | 39 | 1. Approximately 50 percent of patients are evenly distributed between males and females. 40 | 2. Aetna is the preferred insurance provider for the majority of patients. 41 | 3. Nearly 33 percent of patients require urgent admission. 42 | 4. Penicillin is the recommended medication for a higher number of patients. 43 | 5. Michael Johnson, a patient, has been admitted 7 times, indicating a higher frequency of medical needs. 44 | 6. The hospital of choice for most patients is Smith PLC. 45 | --------------------------------------------------------------------------------