Project: Statistical Methods for Data Science
Topic: Statistical Methods for Heart Disease & Indicators
Keywords: Data Science, Statistical & Inferential Analysis, Exploratory Data Analysis (EDA), Regression Analysis, Heart Disease.
- Use staitstical analyses to identify which features (Age, Sex, ChestPainType, etc.) have any significance when classifying heart disease patients.
- The dataset can be obtained here from Kaggle.
- RStudio(R) is used to assist this project with statistical analyses by extracting important insights using:
- In the healthcare industry, understanding what factors or indicators affect a disease is an essential part of the decision-making and problem-solving process.
- People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model or statistical analyses can be of great help.
- These indicators allow decision-makers to identify any potential ways to reduce risk factors of future health and increase the likelihood of disease prevention effectively (Santos et al., 2019).
- Aim:
- To improve the process of analyzing patients’ heart disease in the healthcare industry to allow earlier detection and avoidance of heart disease and morbidity.
- Objective:
- To create a statistical model that identifies which factors/data variables are significant based on the relevance of statistical inferences when classifying heart disease patients.
- The insights gained by analyzing the statistical inference of each data variable to the target data will aid in establishing which factor or indicator is critical in causing heart disease.
(1) HeartDisease_Dataset.csv
- Heart Disease dataset file in CSV format.
(2) HeartDisease_EDA-StatisticalAnalysis_R Folder
- Contains the main R program with implementation codes and explanations for the project.
- None (for now)
- Took inspiration from Kaggle