Anas1108 / Predicting-Mortality-and-Cardiovascular-Diseases

A dataset containing 12 features for predicting mortality by heart failure caused by Cardiovascular Diseases (CVDs) is analyzed. The dataset includes demographic information such as gender, age, and presence of risk factors like diabetes, anemia, high blood pressure, and smoking habits.

Cardiovascular Diseases and Predicting Mortality

Introduction

Cardiovascular diseases (CVDs) are the leading cause of death globally, claiming an estimated 17.9 million lives each year, accounting for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.

Data Features

• Sex (Male = 1, Female = 0)
• Age
• Diabetes (0 = No, 1 = Yes)
• Anemia (0 = No, 1 = Yes)
• High blood pressure (0 = No, 1 = Yes)
• Smoking (0 = No, 1 = Yes)
• DEATH_EVENT (0 = No, 1 = Yes)

Hypothesis Testing

To reach a conclusion and predict mortality, hypothesis testing will be used to answer the following questions:

1. Are diabetic individuals more likely to have high blood pressure?
2. Are individuals with anemia more likely to be female?
3. Are individuals between the ages of 55 and 65 with high blood pressure less likely to survive?
4. Are diabetic individuals who smoke less likely to survive?
5. Are male individuals more likely to get diabetic?

Dos and Don'ts

Ensure that your conclusions are sound by keeping in mind the dos and don'ts of hypothesis testing, such as sample size, randomization, etc.

Results

All intermediate steps, graphs, and experimental results must be included while answering the questions.